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Enroll Here: Deep Learning with TensorFlow Cognitive Class Exam Quiz Answers

Introduction to Deep Learning with TensorFlow

Deep Learning with TensorFlow is an exciting field that combines neural networks with the powerful TensorFlow framework, allowing you to build and train advanced neural network models for various tasks like image classification, natural language processing, and more. Here’s a brief introduction:

What is TensorFlow?

TensorFlow is an open-source machine learning library developed by Google. It allows you to build and train machine learning models, particularly neural networks, efficiently. TensorFlow provides a flexible ecosystem of tools, libraries, and community resources that help researchers and developers build and deploy ML models easily.

What is Deep Learning?

Deep Learning is a subset of machine learning where artificial neural networks, inspired by the human brain’s structure and function, learn from large amounts of data. These networks have multiple layers (hence “deep”) that allow them to learn hierarchical representations of data.

Key Concepts in Deep Learning with TensorFlow:

1. TensorFlow Operations (Ops) and Graphs: TensorFlow uses a dataflow graph to represent your computation in terms of the dependencies between individual operations (Ops). These Ops create, manipulate, and destroy tensors (multi-dimensional arrays) during execution.
2. Neural Networks: Deep Learning in TensorFlow primarily involves constructing neural network architectures using high-level APIs like Keras (which is integrated into TensorFlow). Neural networks are composed of layers of neurons that process and transform data.
3. Training and Optimization: TensorFlow provides optimization algorithms and techniques to train neural networks on large datasets. This includes defining loss functions, selecting appropriate optimizers (e.g., SGD, Adam), and monitoring training performance.
4. Deployment: After training, TensorFlow allows you to deploy your models in various environments, from mobile devices to cloud platforms, using TensorFlow Serving or TensorFlow Lite for mobile and embedded devices.

Getting Started with TensorFlow:

To start with Deep Learning and TensorFlow, you typically follow these steps:

• Installation: Install TensorFlow and its dependencies (can be done via pip).
• Hello World: Begin with simple examples like training a neural network on the MNIST dataset (handwritten digit classification).
• Exploration: Understand different layers, activation functions, and how to manipulate tensors.
• Advanced Topics: Explore more advanced topics like transfer learning, custom models, and deploying models.

Conclusion:

Deep Learning with TensorFlow opens up a world of possibilities in artificial intelligence and machine learning. Whether you’re a beginner or an experienced practitioner, TensorFlow offers the tools and resources to build and deploy sophisticated models effectively. As you dive deeper, you’ll find yourself exploring cutting-edge research and applications in this rapidly evolving field.

Deep Learning with TensorFlow Cognitive Class Certificate Answers

Module 1 – Intro to Tensorflow Quiz Answers

Question 1: Which statement is FALSE about TensorFlow?

• TensorFlow is well suited for handling Deep Learning Problems
• TensorFlow library is not proper for handling Machine Learning Problems
• TensorFlow has a C/C++ backend as well as Python modules
• TensorFlow is an opensource library
• All of the above

Question 2: What is a Data Flow Graph?

• A representation of data dependencies between operations
• A cartesian (x,y) chart
• A graphics user interface
• A flowchart describing an algorithm
• None of the above

Question 3: What is the main reasons of increasing popularity of Deep Learning?

• The advances in machine learning algorithms and research.
• The availability of massive amounts of data for training computer systems.
• The dramatic increases in computer processing capabilities.
• All of the above

Question 4: Which statement is TRUE about TensorFlow?

• Runs on CPU and GPU
• Runs on CPU only
• Runs on GPU only

Question 5: Why is TensorFlow the proper library for Deep Learning?

• It will benefit from TensorFlow’s auto-differentiation and suite of first-rate optimizers
• It provides a collection of trainable mathematical functions that are useful for neural networks.
• It has extensive built-in support for deep learning
• All of the above

Module 2 – Convolutional Networks Quiz Answers

Question 1: What can be achieved with “convolution” operations on Images?

• Noise Filtering
• Image Smoothing
• Image Blurring
• Edge Detection
• All of the above

Question 2: For convolution, it is better to store images in a TensorFlow Graph as:

• Placeholder
• CSV file
• Numpy array
• Variable
• None of the above

Question 3: Which of the following statements is TRUE about Convolution Neural Networks (CNNs)?

• CNN can be applied ONLY on Image and Text data
• CNN can be applied on ANY 2D and 3D array of data
• CNN can be applied ONLY on Text and Speech data
• CNN can be applied ONLY on Image data
• All of the above

Question 4: Which of the following Layers can be part of Convolution Neural Networks (CNNs)

• Dropout
• Softmax        
• Maxpooling
• Relu
• All of the above

Question 5: The objective of the Activation Function is to:

• Increase the Size of the Network
• Handle Non-Linearity in the Network
• Handle Linearity in the Network
• Reduce the Size of the Network
• None of the above

Module 3 – Recurrent Neural Networks Quiz Answers

Question 1: What is a Recurrent Neural Network?

• A Neural Network that can recur to itself, and is proper for handling sequential data
• An infinite layered Neural Network which is proper for handling structured data
• A special kind of Neural Network to predict weather
• A markovian model to handle temporal data

Question 2: What is NOT TRUE about RNNs?

• RNNs are VERY suitable for sequential data.
• RNNs need to keep track of states, which is computationally expensive.
• RNNs are very robust against vanishing gradient problem.

Question 3: What application(s) is(are) suitable for RNNs?

• Estimating temperatures from weather data
• Natural Language Processing
• Video context retriever
• Speech Recognition
• All of the above

Question 4: Why are RNNs susceptible to issues with their gradients?

• Numerical computation of gradients can drive into instabilities
• Gradients can quickly drop and stabilize at near zero
• Propagation of errors due to the recurrent characteristic
• Gradients can grow exponentially
• All of the above

Question 5: What is TRUE about LSTM gates?

• The Read Gate in LSTM, determine how much old information to forget
• The Write Gate in LSTM, reads data from the memory cell and sends that data back to the network.
• The Forget Gate, in LSTM maintains or deletes data from the information cell.
• The Read Gate in LSTM, is responsible for writing data into the memory cell.

Module 4 – Restricted Boltzmann Machine Quiz Answers

Question 1: What is the main application of RBM?

• Data dimensionality reduction
• Feature extraction
• Collaborative filtering
• All of the above

Question 2: How many layers does an RBM (Restricted Boltzmann Machine) have?

• Infinite
• 4
• 2
• 3
• All of the above

Question 3: How does an RBM compare to a PCA?

• RBM cannot reduce dimensionality
• PCA cannot generate original data
• PCA is another type of Neural Network
• Both can regenerate input data
• All of the above

Question 4: Which statement is TRUE about RBM?

• It is a Boltzmann machine, but with no connections between nodes in the same layer
• Each node in the first layer has a bias
• The RBM reconstructs data by making several forward and backward passes between the visible and hidden layers
• At the hidden layer’s nodes, X is multiplied by a W (weight matrix) and added to h_bias
• All of the above

Question 5: Which statement is TRUE statement about an RBM?

• The objective function is to maximize the likelihood of our data being drawn from the reconstructed data distribution
• The Negative phase of an RBM decreases the probability of samples generated by the model
• Contrastive Divergence (CD) is used to approximate the negative phase of an RBM
• The Positive phase of an RBM increases the probability of training data
• All of the above

Module 5 – Autoecoders Quiz Answers

Question 1: What is the difference between Autoencoders and RBMs?

• Autoencoders are used for supervised learning, but RBMs are used for unsupervised learning.
• Autoencoders use a deterministic approach, but RBMs use a stochastic approach.
• Autoencoders have less layers than RBMs.
• All of the above

Question 2: Which of the following problems cannot be solved by Autoencoders:

• Dimensionality Reduction
• Time series prediction
• Image Reconstruction
• Emotion Detection
• All of the above

Question 3: What is TRUE about Autoencoders:

• Help to Reduce the Curse of Dimensionality
• Used to Learn the Most important Features in Data
• Used for Unsupervised Learning
• All of the Above

Question 4: What are Autoencoders:

• A Neural Network that is designed to replace Non-Linear Regression
• A Neural Network that is trained to attempt to copy its input to its output
• A Neural Network that learns all the weights by using labelled data
• A Neural Network where different layer inputs are controlled by gates
• All of the Above

Question 5: What is a Deep Autoencoder:

• An Autoencoder with Multiple Hidden Layers
• An Autoencoder with multiple input and output layers
• An Autoencoder stacked with Multiple Visible Layers
• An Autoencoder stacked with over 1000 layers
• None of the Above

Deep Learning with TensorFlow Final Exam Answers

Question 1: Why use a Data Flow graph to solve Mathematical expressions?

• To create a pipeline of operations and its corresponding values to be parsed
• To represent the expression in a human-readable form
• To show the expression in a GUI
• Because it is the only way to solve mathematical expressions in a digital computer
• None of the above

Question 2: What is an Activation Function?

• A function that triggers a neuron and generates the outputs
• A function that models a phenomenon or process
• A function to normalize the output
• All of the above
• None of the above

Question 3: Why is TensorFlow considered fast and suitable for Deep Learning?

• It is suitable to operate over large multi-dimensional tensors
• It runs on CPU
• Its core is based on C++
• It runs on GPU
• All of the above

Question 4: Can TensorFlow replace Numpy?

• None of the above
• No, whatsoever
• With only Numpy we can’t solve Deep Learning problems, therefore, TensorFlow is required
• Yes, completely
• Partially for some operations on tensors, such as minimization

Question 5: What is FALSE about Convolution Neural Networks (CNNs)?

• They fully connect to all neurons in all of the layers
• They connect only to neurons in the local region (kernel size) of input images
• They build feature maps hierarchically in every layer
• They are inspired by human visual systems
• None of the above

Question 6: What is the meaning of “Strides” in Maxpooling?

• The number of pixels the kernel should add
• The number of pixels the kernel should move
• The size of the kernel
• The number of pixels the kernel should remove
• None of the above

Question 7: What is TRUE about “Padding” in Convolution?

• size of the input image is reduced for the “VALID” padding
• Size of the input image is reduced for the “SAME” padding
• Size of the input image is increased for the “SAME” padding
• Size of the input image is increased for the “VALID” padding
• All of the above

Question 8: Which of the following best describes the Relu Function?

• (-1,1)
• (0,5)
• (0, Max)
• (-inf,inf)
• (0,1)

Question 9: Which are types of Recurrent Neural Networks? (Select all that apply)

• LSTM
• Hopfield Network
• Recursive Neural Network
• Deep Belief Network
• Elman Networks and Jordan Networks

Question 10: Which is TRUE about RNNs?

• RNNs can predict the future
• RNNs are VERY suitable for sequential data
• RNNs are NOT suitable for sequential data
• RNNs are ONLY suitable for sequential data
• All of the above

Question 11: What is the problem with RNNs and gradients?

• Numerical computation of gradients can drive into instabilities
• Gradients can quickly drop and stabilize at near zero
• Propagation of errors due to the recurrent characteristic
• Gradients can grow exponentially
• All of the above

Question 12: What type of RNN would you use in an NLP project to predict the next word in a phrase? (only one is correct)

• Bi-directional RNN
• Neural history compressor
• Long Short-Term Memory
• Echo state network
• None of the above

Question 13: Which one does NOT happen in the “forward pass” in RBM?

• Making a deterministic decision about returning values into network.
• Multiplying inputs by weights, and adding an overall bias, in each hidden unit.
• Applying an activation function on the results in hidden units.
• Feeding the network with the input images converted to binary values.

Question 14: Which one IS NOT a sample of CNN application?

• Creating art images using pre-trained models
• Object Detection in images
• Coloring black and white images
• Predicting next word in a sentence

Question 15: Select all possible uses of Autoencoders and RBMs (select all that apply):

• Clustering
• Pattern Recognition
• Dimensionality Reduction
• Predict data in time series

Question 16: Which technique is proper for solving Collaborative Filtering problem?

• DBN
• RBM
• CNN
• RNN

Question 17: Which statement is TRUE for training Autoencoders?

• The Size of Last Layer must be at least 10% of the Input Layer Dimension
• The size of input and Last Layers must be of the Same Dimensions
• The Last Layer must be Double the size of Input Layer Dimension
• The Last Layer must be half the size of Input Layer Dimension
• None of the Above

Question 18: To Design a Deep Autoencoder Architecture, what factors are to be considered?

• The size of the centre-most layer has to be close to number of Important Features to be extracted
• The centre-most layer should have the smallest size compared to all other layers
• The Network should have an odd number of layers
• All the layers must be symmetrical with respect to the centre-most layer
• All of the Above

Question 19: With is TRUE about Back-Propogation?

• It can be used to train LSTMs
• It can be used to train CNNs
• It can be used to train RBMs
• It can be used to train Autoencoders
• All of the Above

Question 20: How can Autoencoders be improved to handle highly non-linear data?

• By using Genetic Algorithms
• By adding more Hidden Layers to the Network
• By using Higher initial Weight Values
• By using Lower initial Weight Values
• All of the Above

Enroll Here: Data Visualization with Python Cognitive Class Exam Quiz Answers

Introduction to Data Visualization with Python

Data visualization with Python is a powerful way to understand and communicate insights from data. Python offers several libraries, each with its strengths, for creating visually appealing and informative plots.

Libraries for Data Visualization in Python:

1. Matplotlib: This is a widely-used 2D plotting library that produces publication-quality figures. It provides a MATLAB-like interface and can create a wide variety of plots, including line plots, scatter plots, bar plots, histograms, etc.
2. Seaborn: Built on top of Matplotlib, Seaborn provides a high-level interface for drawing attractive and informative statistical graphics. It simplifies complex visualizations and works seamlessly with Pandas DataFrames.
3. Plotly: Known for creating interactive plots, Plotly is great for web-based visualizations. It can generate a wide range of plots, including basic charts, statistical plots, 3D plots, and geographical plots.
4. Bokeh: Bokeh is another library for interactive visualizations, but it focuses more on providing interactive plots for web browsers. It’s designed for creating complex plots with large datasets.
5. Pandas Visualization: Pandas itself provides basic plotting capabilities directly from DataFrames and Series objects. It’s handy for quick exploratory plots and integrates well with data manipulation tasks.

Getting Started with Data Visualization in Python:

To begin with data visualization in Python, you typically follow these steps:

1. Install Required Libraries: Use pip or conda to install Matplotlib, Seaborn, Plotly, Bokeh, and any other libraries you want to use.
2. Load Your Data: Import your data into Python. Pandas is commonly used for this purpose (import pandas as pd).
3. Choose a Library: Depending on your visualization needs (static vs. interactive, type of plot, etc.), choose the appropriate library (e.g., Matplotlib for basic plots, Seaborn for statistical visualizations, Plotly for interactive plots).
4. Create Your Plot: Use the chosen library to create your plot. Each library has its syntax and functions for creating different types of plots.
5. Customize and Annotate: Customize your plot as needed: add labels, titles, legends, change colors, etc. Annotations help in conveying insights clearly.
6. Display or Save Your Plot: Depending on your environment (Jupyter Notebook, standalone script, web application), display your plot (plt.show() for Matplotlib, fig.show() for Plotly) or save it to a file (plt.savefig() in Matplotlib).

Conclusion:

Data visualization in Python is a fundamental skill for data scientists and analysts. Choosing the right library depends on the complexity of the data and the type of visualization you want to create. With practice and exploration, you can create compelling visualizations that effectively communicate insights from your data.

Data Visualization with Python Cognitive Class Certificate Answers

Module 1 – Introduction to Visualization Tools Quiz Answers

Question 1: What are the layers that make up the Matplotlib architecture?

• FigureCanvas Layer, Renderer Layer, and Artist Layer.
• Backend_Bases Layer, Artist Layer, Scripting Layer.
• Backend Layer, Artist Layer, and Scripting Layer.
• Backend Layer, FigureCanvas Layer, Renderer Layer, Artist Layer, and Scripting Layer.
• Figure Layer, Artist Layer, and Scripting Layer.

Question 2: Using the inline backend, you can modify a figure after it is rendered.

• False
• True

Question 3: Which of the following are examples of Matplotlib magic functions? Choose all that apply.

• %matplotlib inline
• #matplotlib notebook
• $matplotlib outline
• %matplotlib notebook
• #matplotlib inline

Module 2 – Basic Visualization Tools Quiz Answers

Question 1: Area plots are stacked by default.

• False
• True

Question 2: Given a pandas series, series_data, which of the following will create a histogram of series_data and align the bin edges with the horizontal tick marks?

• count, bin_edges = np.histogram(series_data)
  • series_data.plot(kind=’hist’, xticks = count, bin_edges)
• count, bin_edges = np.histogram(series_data)
  • series_data.plot(kind=’hist’, xticks = count)
• count, bin_edges = np.histogram(series_data)
  • series_data.plot(kind=’hist’, xticks = bin_edges)
• series_data.plot(kind=’hist’)
• count, bin_edges = np.histogram(series_data)
  • series_data.plot(type=’hist’, xticks = bin_edges)

Question 3: Given a pandas dataframe, question, which of the following will create a horizontal barchart of the data in question?

• question.plot(type=’bar’, rot=90)
• question.plot(kind=’bar’, orientation=’horizontal’)
• question.plot(kind=’barh’)
• question.plot(kind=’bar’)
• question.plot(kind=’bar’, type=’horizontal’)

Module 3 – Specialized Visualization Tools Quiz Answers

Question 1: Pie charts are less confusing than bar charts and should be your first attempt when creating a visual.

• False
• True

Question 2: What do the letters in the box plot above represent?

• A = Mean, B = Upper Mean Quartile, C = Lower Mean Quartile, D = Inter Quartile Range, E = Minimum, and F = Outliers
• A = Mean, B = Third Quartile, C = First Quartile, D = Inter Quartile Range, E = Minimum, and F = Outliers
• A = Median, B = Third Quartile, C = First Quartile, D = Inter Quartile Range, E = Minimum, and F = Outliers
• A = Median, B = Third Quartile, C = Mean, D = Inter Quartile Range, E = Lower Quartile, and F = Outliers
• A = Mean, B = Third Quartile, C = First Quartile, D = Inter Quartile Range, E = Minimum, and F = Maximum

Question 3: What is the correct combination of function and parameter to create a box plot in Matplotlib?

• Function = box, and Parameter = type, with value = “plot”
• Function = boxplot, and Parameter = type, with value = “plot”
• Function = plot, and Parameter = type, with value = “box”
• Function = plot, and Parameter = kind, with value = “boxplot”
• Function = plot, and Parameter = kind, with value = “box”

Module 4 – Advanced Visualization Tools Quiz Answers

Question 1: Which of the choices below will create the following regression line plot, given a pandas dataframe, data_dataframe?

• import seaborn as sns
  • ax = sns.regplot(x=”year”, y=”total”, data=data_dataframe, color=”green”)
• data_dataframe.plot(kind=”regression”, color=”green”, marker=”+”)
• import seaborn as sns
  • ax = sns.regplot(x=”year”, y=”total”, data=data_dataframe, color=”green”, marker=”+”)
• data_dataframe.plot(kind=”regplot”, color=”green”, marker=”+”)
• import seaborn as sns
  • ax = sns.regplot(x=”total”, y=”year”, data=data_dataframe, color=”green”)

Question 2: In Python, creating a waffle chart is straightforward since we can easily create one using the scripting layer of Matplotlib.

• False
• True

Question 3: A word cloud (choose all that apply)

• is a depiction of the frequency of different words in some textual data.
• is a depiction of the frequency of the stopwords, such as a, the, and, in some textual data.
• is a depiction of the meaningful words in some textual data, where the more a specific word appears in the text, bigger and bolder it appears in the word cloud.
• can be generated in Python using the word_cloud library that was developed by Andreas Mueller.
• can be easily created using Matplotlib using the scripting layer.

Module 5 – Creating Maps and Visualizing Geospatial Data Quiz Answers

Question 1: What tile style of Folium maps is usefule for data mashups and exploring river meanders and coastal zones?

• OpenStreetMap
• Mapbox Bright
• Stamen Toner
• Stamen Terrain
• River and Coastal

Question 2: You cluster markers superimposed onto a map in Folium using a feature group object.

• False
• True

Question 3: If you know that the latitude of Spain is 40.4637° N and its longitude is 3.7492° W, and you are interested in generating a map of Spain to visualize its hill shading and natural vegetation. Which of the following lines of code will create the right map for you?

• folium.Map(location=[40.4637, 3.7492], zoom_start=6, tiles=’Stamen Toner’)
• folium.Map(location=[40.4637, 3.7492], zoom_start=6, tiles=’Stamen Terrain’)
• folium.Map(location=[40.4637, -3.7492], zoom_start=6, tiles=’Stamen Terrain’)
• folium.Map(location=[-40.4637, -3.7492], zoom_start=6, tiles=’Stamen Terrain’)
• folium.Map(location=[40.4637, 3.7492], zoom_start=6)

Data Visualization with Python Final Exam Answers

Question 1: Data visualizations are used to (check all that apply):

• explore a given dataset.
• perform data analytics and build predictive models.
• train and test a machine learning algorithm.
• share unbiased representation of data.
• support recommendations to different stakeholders.

Question 2: Matplotlib was created by John Hunter, an American neurobiologist, and was originally developed as an EEG/ECoG visualization tool.

• False
• True

Question 3: One type of Artist object is the primitive type. Which of the following are examples of the primitive type? Check all that apply.

• Rectangle
• Figure
• Axes
• Circle
• Text

Question 4: Using the notebook backend, you can modify a figure after it is rendered.

• False
• True

Question 5: The scripting layer is (check all that apply):

• comprised mainly of pyplot.
• an area on which the figure is drawn.
• a handler of user inputs such as keyboard strokes and mouse clicks.
• lighter that the Artist layer, and is intended for scientists whose goal is to perform quick exploratory analysis.
• comprised one one main object – Artist.

Question 6: Which of the following are instances of the Artist object? Check all that apply.

• Titles
• Event
• FigureCanvas
• Tick Labels
• Images

Question 7: There are three types of Artist objects.

• False
• True

Question 8: Each primitive artist may contain other composite artists as well as primitive artists.

• False
• True

Question 9: Given a pandas dataframe, question, which of the following will create an unstacked area plot of the data in question?

• question.plot(type=’area’, stacked=False)
• question.plot(kind=’area’, unstacked=True)
• question.plot(kind=’area’, stacked=False)
• question.plot(kind=’area’)
• question.plot(type=’area’, unstacked=True)

Question 10: Pie charts are relevant only in the rarest of circumstances, and bar charts are far superior ways to quickly get a message across.

• False
• True

Question 11: What is the correct function, parameter and value input for creating a pie chart in Matplotlib?

• Function = plot, parameter = kind, value = “pie”
• Function = pie, parameter = type, value = “plot”
• Function = plot, parameter = type, value = “pie”
• Function = pie, parameter = kind, value = “plot”

Question 12: What are the five main dimensions of a box plot? Select all that apply.

• Minimum
• Standard Deviation
• Maximum
• First Quartile
• Third Quartile
• Median
• Skewness

Question 13: Which of the lines of code below will create the following scatter plot, given the pandas dataframe, df_total?

• import matplotlib.pyplot as plt
  • plot(kind=’scatter’, x=’year’, y=’total’, data=df_total)
  • plt.title(‘Total Immigrant population to Canada from 1980 – 2013’)
  • plt.label (‘Year’)
  • plt.label(‘Number of Immigrants’)
• import matplotlib.pyplot as plt
  • df_total.plot(type=’scatter’, x=’year’, y=’total’)
  • plt.title(‘Total Immigrant population to Canada from 1980 – 2013’)
  • plt.label (‘Year’)
  • plt.label(‘Number of Immigrants’)
• import matplotlib.pyplot as plt
  • df_total.plot(kind=’scatter’, x=’year’, y=’total’)
  • plt.title(‘Total Immigrant population to Canada from 1980 – 2013’)
  • plt.xlabel (‘Year’)
  • plt.ylabel(‘Number of Immigrants’)
• import matplotlib.scripting.pyplot as plt
  • df_total.plot(kind=’scatter’, x=’year’, y=’total’)
  • plt.title(‘Total Immigrant population to Canada from 1980 – 2013’)
  • plt.label (‘Year’)
  • plt.label(‘Number of Immigrants’)
• import matplotlib.scripting.pyplot as plt
  • df_total.plot(type=’scatter’, y=’year’, x=’total’)
  • plt.title(‘Total Immigrant population to Canada from 1980 – 2013’)
  • plt.xlabel (‘Year’)
  • plt.ylabel(‘Number of Immigrants’)

Question 14: A bubble plot is a variation of the scatter plot that displays three dimensions of data.

• False
• True

Question 15: Seaborn is a Python visualization library that is built on top of Matplotlib.

• False
• True

Question 16: Which of the choices below will create the following regression line plot, given a pandas dataframe, data_dataframe?

• import seaborn as sns
  • ax = sns.regplot(x=”year”, y=”total”, data=data_dataframe, color=”green”)
• data_dataframe.plot(kind=”regression”, color=”green”, marker=”+”)
• import seaborn as sns
  • ax = sns.regplot(x=”year”, y=”total”, data=data_dataframe, color=”green”, marker=”+”)
• data_dataframe.plot(kind=”regplot”, color=”green”, marker=”+”)
• import seaborn as sns
  • ax = sns.regplot(x=”total”, y=”year”, data=data_dataframe, color=”green”)

Question 17: Which of the following can be accomplished with the package word_cloud in Python? Select all that apply.

• Create a word cloud based on the frequency of different words in some textual data.
• Create a bubble plot based on the word cloud.
• Superimpose the words in a word cloud onto the mask of any shape.
• Import default stop words.

Question 18: The following are tile styles of folium maps (choose all that apply).

• Stamen Terrain
• River Coastal
• Stamen Toner
• Mapbox Bright
• Open Stamen

Question 19: You cluster markers superimposed onto a map in Folium using a marker cluster object.

• False
• True

Question 20: If you know that the latitude and the longitude of Spain are 40.4637° N and 3.7492° W, respectively, and you are interested in generating a map of Spain to explore its river meanders and coastal zones. Which of the following lines of code will create the right map for you?

• folium.Map(location=[40.4637, 3.7492], zoom_start=6, tiles=’Stamen Terrain’)
• folium.Map(location=[40.4637, 3.7492], zoom_start=6, tiles=’Stamen Toner’)
• folium.Map(location=[40.4637, -3.7492], zoom_start=6, tiles=’Stamen Toner’)
• folium.Map(location=[-40.4637, -3.7492], zoom_start=6, tiles=’Stamen Terrain’)
• folium.Map(location=[40.4637, 3.7492], zoom_start=6)

Enroll Here: Data Visualization with R Cognitive Class Exam Quiz Answers

Introduction to Data Visualization with R

Data visualization is the graphical representation of data and information. It enables us to discover patterns, trends, and insights that might be hidden in raw data. R provides powerful tools and libraries for creating a wide variety of visualizations.

Why Use R for Data Visualization?

R is a popular programming language for statistical computing and graphics. It offers several advantages for data visualization:

1. Rich Ecosystem: R has a vast collection of packages specifically designed for data visualization, such as ggplot2, plotly, ggvis, leaflet, and more.
2. Flexibility: R provides extensive capabilities to customize and control the visual appearance of plots, allowing you to create complex and publication-quality graphics.
3. Integration: R seamlessly integrates data manipulation, statistical analysis, and visualization, making it ideal for end-to-end data analysis workflows.

Getting Started with R for Data Visualization

To start visualizing data in R, follow these steps:

1. Install R and RStudio: RStudio is a popular integrated development environment (IDE) for R that makes it easier to work with R projects and visualize data.
2. Load Data: Import your data into R using functions like read.csv() for CSV files, read.table() for tabular data, or directly loading data from R datasets.
3. Choose a Visualization Package: Depending on the type of visualization you want to create, choose an appropriate package. For example:
   • ggplot2: For creating elegant and customizable static plots.
   • plotly: For interactive and web-based visualizations.
   • ggvis: For interactive grammar of graphics.
   • leaflet: For interactive maps.
4. Create Plots: Use functions provided by your chosen package to create visualizations. For ggplot2, start with ggplot() function and layer on aesthetics and geometries.
5. Customize: Adjust the aesthetics (like colors, sizes, labels) and add layers (like points, lines, bars) to tailor the plot to your needs.
6. Save and Share: Save your plots in various formats (PNG, PDF, etc.) and share them in reports, presentations, or web applications.

Data visualization in R is a dynamic and rewarding skill that opens doors to exploring and communicating insights from data effectively.

Data Visualization with R Cognitive Class Certification Answers

Module 1 – Basic Visualization Tools Quiz Answers

Question 1: Choose the correct code from the following options to install the ggplot2 library in R.

• install.packages(ggplot2)
• install.package(“ggplot2”)
• install.packages(“ggplot2”)
• install.package(ggplot2)

Question 2: Select the correct code that provides numerical values to different segments of the pie chart?

• coord_polar(theta=’y’)
• coord_polar(‘y’)
• theta = coord_polar(‘y’)
• coord_polar()

Question 3: Select the correct code from the following options to remove the color of the bars in the histogram?

• xlab = 0
• alpha = I(0)
• binwidth = 0
• geom = 0

Module 2 – Basic Visualization Tools Continued Quiz Answers

Question 1: Select the correct methods used to create a scatterplot in R?

• scatterplot3d()
• scatterplot()
• qplot()
• plot()
• All the above options are correct

Question 2: Select the correct parameter used to change the line width in a line graph using the geom_line() method?

• line_size
• size
• line_width
• width

Question 3: Which parameter out of the following represents the Confidence Interval in Regression?

• confidence_interval
• se
• ci
• lm

Module 3 – Specialized Visualization Tools Quiz Answers

Question 1: Select the correct option for the values of Median, Lower whisker, Upper whisker, First quartile, Third quartile for the dataset: 3, 5, 7, 8, 12, 13, 14, 18, 21?

• 33, 2, 5, 12, 18
• 6, 2, 22, 7, 2
• 12, 4, 2, 3, 17
• 12, 3, 21, 6, 16

Question 2: >> True or False: To create waffle charts in R, we use ggplot library which is used for graphs and also waffle library.

• True
• False

Question 3: When working with Radar Charts in R, what is the correct code used to suppress the warnings generated?

• (warn = -1)
• (warn = 1)
• options(warn = -1)
• None of the options are correct

Module 4 – How to Create Maps Quiz Answers

Question 1: Which function gives an object that represents an empty world map.

• addMarkers
• addTiles
• leaflet
• None of the options are correct

Question 2: What is the use of the method addTiles() in R?

• To divide map in tiles.
• To zoom in the map to see different countries and streets in detail.
• To create tiles view of map.
• All the options above are correct

Question 3: ___________ method is used to publish a tile layer on the map.

• leaf()
• leaflet()
• addTiles()
• None of the above options are correct

Module 5 – How to Build Interactive Web Pages Quiz Answers

Question 1: Which function is used to create the display of shiny app?

• display()
• output functions
• fluidPage()
• input functions

Question 2: Which argument is used to position sliderbar on the right hand side in shiny app?

• align=”right”
• position=”right”
• position = “rightalign”
• sliderLayout(position=”right”)

Question 3: >>What does the ui.R file in R contain?

• inputs for server.R
• layout and appearance of app.
• instructions to build output object.
• All the above options are correct

Data Visualization with R Final Exam Answers

Question 1: Which of the following options indicate the features that make R suitable for Data Visualizations

• R is a free and open source tool
• Built for statistical computing
• Visualization Tools
• All the options above are correct

Question 2: if(“ggplot2” %in% rownames(installed.packages()) == FALSE) {install.packages(“ggplot2”)}library(ggplot2) Please select the correct explanation for the code from the following options

• Check if “ggplot2” library is already installed into R, if not then install it and load it into R environment
• Load the “ggplot2”library into R environment
• Install and load the “ggplot2” library into R environment
• Install the “ggplot2” library into R

Question 3: We want to create a Bar Graph with black filled bars, x and y axis labels as Cylinders and Number of cars respectively and outline color of bars as Red. Choose the correct code block from below which will create the desired graph.

• qplot(mtcars$cyl, geom = “bar”, fill = I(“black”), xlab = “Cylinders”, ylab = “Number of Cars”, colour = I(“red”))
• qplot(mtcars$cyl, geom = “histogram”, fill = I(“black”), xlab = “Cylinders”, ylab = “Number of Cars”, colour=I(“red”))
• qplot(mtcars$cyl, fill = I(“black”), xlab = “Cylinders”, ylab=”Number of Cars”, outline=I(“red”)
• None of the options are correct

Question 4: Choose the correct parameter which will define the width of the bars in a Histogram.

• binwidth
• barWidth
• bar_width(value)
• width
• None of the options are correct

Question 5: A pie chart in ggplot2 is a transformed stacked bar plot. Select the correct function from the following options to convert a stacked bar plot into a pie chart?

• pieChart (theta=’y’)
• coord_polar(theta=’y’)
• transform(theta=’y’)
• None of the above options are correct

Question 6: Let’s assume we have the mtcars dataset and we want to create a Scatter Plot by using mpg and wt columns of this dataset. Select the correctcode from the options below which can create the plot for us with red colored points.

• ggplot(mtcars,aes(x=mpg,y=wt)) + geom_point()
• ggplot(mtcars,aes(x=mpg,y=wt)) + geom_point(shape=19, colour=”red”)
• ggplot(mtcars,aes(x=mpg,y=wt)) + geom_point(shape=19, “blue”)
• None of the options above are correct

Question 7: True or False: It is possible plot more than one line at a time in the same graph using the ggplot() method

• False
• True

Question 8: Choose the correct statement from the following options

• se parameter is used to display confidence interval
• There is no term Confidence Interval in Regression
• If you want to display confidence interval then either just ignore the se attribute, or set it to TRUE
• None of above options are correct

Question 9: Suppose we want to download a .csv file from the link below, https://ibm.box.com/shared/static/cmid70rpa7xe4ocitcga1bve7r0kqnia.csv and store it with name my_data.csv in “/resources/data/” folder. Select the correct code from the options below

• file(“https://ibm.box.com/shared/static/cmid70rpa7xe4ocitcga1bve7r0kqnia.csv”, “/resources/data/my_data.csv”)
• download.file(“https://ibm.box.com/shared/static/cmid70rpa7xe4ocitcga1bve7r0kqnia.csv”, destfile = “/resources/data/my_data.csv”)
• download(“https://ibm.box.com/shared/static/cmid70rpa7xe4ocitcga1bve7r0kqnia.csv”, destfile = “/resources/data/my_data.csv”)
• None of the options are correct

Question 10: Select the correct format from the following options to create a word cloud with a minimum frequency value 1 and maximum words 200?

• wordcloud(words = data, freq = frequency, min.freq = 1, max.words=200)
• wordcloud(data, frequency, minimun= 1, max.words=200)
• wordcloud(freq = frequency, min.freq = 1, maxWords=200)
• wordcloud(words = data, freq = frequency, min.freq = 1)

Question 11: Select the correct option to create a WordCloud that is centered?

• order = FALSE
• random.order = TRUE
• random.order = FALSE
• random = TRUE

Question 12: dplyr and Scales libraries in a Radar Chart are used for?

• filtering the dataset
• provides convenient viewing and utility functions
• pipe operations and to determine labels respectively
• None of the options are correct

Question 13: Which method is used to avoid displaying warnings, generated while Creating a Radar Chart?

• options(warns=-1)
• warns(-1)
• options(warn=-1)
• warnings(-1)

Question 14: ___________ can be used to adjust the size of Waffle Charts

• kernel
• IRkernel
• Ikernel
• None of the options are correct

Question 15: >> Which of the following parameters are used to create waffle charts in R?

• colors
• rows
• title
• xlab
• All the options above are correct

Question 16: Choose the correct method to create a Box Plot in R

• ggplot(df) + geom_boxplot()
• ggplot(df, aes(x=label, y=value))
• ggplot(df, aes(x=label, y=value)) + geom_boxplot()
• None of the options are correct

Question 17: What is the term used to indicate the difference between the first and third quartiles?

• lower whisker and upper whisker.
• first quartile
• third quartile
• Interquartile Range

Question 18: Which function gives an object that represents an empty world map

• addMarkers
• addTiles
• leaflet
• All the options above are correct

Question 19: What is the use of the method addTiles() in R?

• To divide map in tiles.
• To zoom in the map to see different countries and streets in detail.
• To create tiles view of map.
• All the options above are correct

Question 20: Which function is used to create display of shiny app?

• display()
• output functions
• fluidPage()
• input functions

Enroll Here: Spark Fundamentals II Cognitive Class Exam Quiz Answers

Introduction to Spark Fundamentals II

Spark Fundamentals II typically builds on the foundational concepts covered in Spark Fundamentals I. It usually focuses on more advanced topics and techniques for working with Apache Spark, a powerful open-source distributed computing system.

Here’s a general outline of what might be covered in Spark Fundamentals II:

1. Advanced RDD (Resilient Distributed Dataset) Operations:
   • Deeper understanding of transformations and actions.
   • Performance considerations and optimizations.
   • Using broadcast variables and accumulators.
2. Spark SQL and DataFrames:
   • Introduction to Spark SQL and the DataFrame API.
   • Working with structured data and schemas.
   • Performing SQL queries on DataFrames.
3. Streaming Data with Spark Streaming:
   • Overview of Spark Streaming and its architecture.
   • Processing real-time data streams with Spark.
   • Integrating with external streaming sources (e.g., Kafka, Flume).
4. Machine Learning with MLlib:
   • Introduction to Spark’s MLlib library for machine learning.
   • Building and evaluating machine learning models at scale.
   • Feature engineering and pipeline construction.
5. Graph Processing with GraphX:
   • Understanding graph data structures in Spark.
   • Performing graph computations and algorithms.
   • Applications of graph processing in real-world scenarios.
6. Cluster Management and Deployment:
   • Configuring and managing Spark clusters.
   • Deployment options (standalone, YARN, Mesos).
   • Performance tuning and scalability considerations.
7. Integration with Big Data Ecosystem:
   • Working with other big data technologies (Hadoop, Hive, HBase).
   • Integrating with cloud services (AWS, Azure, Google Cloud).
8. Advanced Topics:
   • Custom Spark applications and libraries.
   • Security and authentication.
   • Monitoring and debugging Spark applications.

Typically, Spark Fundamentals II assumes familiarity with basic concepts like distributed computing, MapReduce paradigm, and possibly some prior experience with Apache Spark or similar frameworks. The course aims to deepen participants’ understanding and proficiency in leveraging Spark for large-scale data processing, analytics, and machine learning tasks.

Spark Fundamentals II Cognitive Class Certification Answers

Module 1: Introduction to Notebooks Quiz Answers

Question 1: Which of the following statements about Zeppelin Notebook is NOT true?

• Zeppelin is open-source.
• With Zeppelin, you can run code and create visualizations through a web interface.
• Zeppelin comes configured with Scala, Spark, and Julia.
• Zeppelin is an interactive data analytics tool started by NFLabs.

Question 2: Jupyter Notebook and Data Scientist Workbench are both open-source projects. True or false?

• False
• True

Question 3: Which notebook will you use in the lab section of this course?

• DataBrick
• Zeppelin
• Watson Studio
• Jupyter Notebook

Module 2: RDD Architecture Quiz Answers

Question 1: Which of the following statements is NOT true?

• Partitioning is what enables parallel execution of Spark jobs.
• An RDD is made up of multiple partitions.
• Spark normally determines the number of partitions based on the size of the hard drives in your cluster.
• Spark is able to read from many different data stores in addition to HDFS, including the local file system and cloud services like Cloudant, AWS, Google, and Azure.

Question 2: In the example of an RDD with 3 partitions and no partitioner, which of the following statements is true?

• It is better not to partition an RDD if you need to join it multiple times.
• Joining RDDs with no partitioner will cause each executor to shuffle all values with the same key to a single machine
• Repeatedly joining on the same RDD is highly efficient.
• The keys are co-located.

Question 3: Speculative execution handles slow tasks by re-launching them as necessary. True or false?

• False
• True

Module 3: Optimizing Transformations and Actions Quiz Answers

Question 1: Which of the following statements is true?

• MapValues applies a map function to each value and performs repartitioning.
• GroupByKey groups all values by key from all partitions into memory.
• GroupByKey shuffles everything and it operates efficiently on large datasets.
• CountByKey is designed to be used in production.

Question 2: AggregateByKey is better than GroupByKey when we want to calculate the average value for each key in an RDD. True or false?

• False
• True

Question 3: Which of the following statements is NOT true?

• MapValues tells Spark that the hashed keys will remain in their partitions and we can keep the same partitioner across operations
• In the example of a pair RDD with 2 partitions, running a map operation over all records will leave the keys of each record unchanged.
• AggregateByKey splits the calculation into two steps. Only one pair per key, per partition is shuffled.
• GroupByKey causes a shuffle of all values across the network, even if they are already co-located within a partition.

Module 4: Caching and Serialization Quiz Answers

Question 1: Which of the following statements is true?

• When you no longer need the persisted RDD, Spark will automatically make room for new RDDs.
• Persisting to disk would allow us to reconstitute the RDD in the event a partition is lost, instead of re-computing all the expensive operations for the lost partitions.
• Ideally we want to persist before any pruning, filtering, or other transformations needed for downstream processing.
• Persisting RDDs can help us save time re-computing partitions, and persistence is in-memory only.

Question 2: Which of the following statements is NOT true?

• Serialization has the added benefit of helping with garbage collection, as you’ll be storing 1 object versus many small objects for each record.
• The records of an RDD will be stored as one large byte array.
• There is almost no CPU usage to deserialize the data.
• Serialization helps by saving space that persisting RDDs occupy in memory.

Question 3: The Java serializer can store the entire RDD in less space than the original file. True or false?

• False
• True

Module 5: Develop and Testing Quiz Answers

Question 1: Which of the following statements is true?

• We cannot use sbt for an Eclipse project.
• We cannot create builds directly from the console using sbt.
• sbt automatically finds source and library files using a conventional directory structure.
• Maven is more powerful and customizable than sbt.

Question 2: IntelliJ fully supports sbt build files with no conversions required. True or false?

• False
• True

Question 3: Which of the following statements is NOT correct during unit testing?

• The spark-testing-base package is handy for testing.
• We want to test the code that is actually used in our application.
• We should not use unit testing tools like scalatest.
• We should put transformations for a given RDD in its own object or class.

Spark Fundamentals II Final Exam Answers

Question 1: Which of the following web-based notebooks is built around Jupyter and iPython?

• Data Scientist Workbench
• Spark Notebook
• Databricks Cloud
• Zeppelin

Question 2: What defines a stage boundary?

• Repartition
• Action
• Transformation
• Shuffle dependency

Question 3: What does RDD stand for?

• Resilient Distributed Dataset
• Reusable Distributed Dataset
• Reusable Data Directory
• None of the above

Question 4: Coalesce can reduce the number of partitions without causing a shuffle. True or false?

• True
• False

Question 5: Which operation should you use to map the values in a pair RDD without affecting the keys or partitions?

• map
• mapValues
• map or mapValues
• You cannot map a pair RDD without affecting the keys or partitions.

Question 6: How can you view the lineage of an RDD?

• showLineage()
• toDebugString()
• printHistory()
• printGraph()

Question 7: Adding a key to an RDD will automatically repartition it so that the keys are co-located. True or false?

• True
• False

Question 8: How can you reference an external class in a closure without serializing it?

• define it as transient
• define it as lazy
• Both of the above
• None of the above

Question 9: What does Spark do during speculative execution?

• Spark looks for tasks it expects to be short and runs them first
• Spark dynamically allocates more resources to large tasks
• Spark identifies slow-running tasks and restarts them
• None of the above

Question 10: What does the following code do?

val text = sc.textFile(“SomeText.txt”)

val counts = text.flatMap(_.split(” “)).map((_, 1)).reduceByKey(_ + _).collectAsMap()

• Counts the total number of words in the document
• Counts the number of distinct words in the document
• Maps every word in the document to the number of times it occurs
• None of the above

Question 11: Which operation has the highest chance of causing out-of-memory errors if the dataset is really large?

• countByValue
• groupByKey
• reduceByKey
• map

Question 12: What is the result of this code?

val pairs = sc.parallelize(List((“a”, 1), (“a”, 5), (“b”, 6), (“b”, 3), (“c”, 2)))

val results = pairs.reduceByKey((a, b) => {

a > b match {

case true => a

case false => b

}

}).collectAsMap()

• (“a” -> 5, “b “ -> 6, “c” -> 2)
• (“a” -> 6, “b “ -> 9, “c” -> 2)
• (5, 6, 2)
• None of the above.

Question 13: You can execute asynchronous actions with the default FIFO scheduler. True or false?

• True
• False

Question 14: Which of the following statements about broadcast variables is true?

• They are read-only
• They can eliminate shuffles
• They are shared between workers via the peer-to-peer protocol
• All of the above
• None of the above

Question 15: With the MEMORY_ONLY storage level, what happens when an RDD can’t fit in memory?

• Spark will automatically change the storage level to MEMORY_AND_DISK
• Some of the partitions will not be cached
• Some of the partitions will be spilled to disk
• Spark will throw an OOM error
• None of the above

Question 16: How can you reduce the amount of memory used by persisted RDDs?

• Use primitive types instead of Java or Scala collections and nested classes
• Enable compression
• Use Kryo serialization instead of Java
• All of the above
• None of the above

Question 17: Which point in an RDD lineage is the best to persist?

• Before a reduceByKey operation
• After outputting to disk
• After a lot of transformations for downstream computations, such as filtering or joining
• At the root RDD
• None of the above

Question 18: A pool can have its own scheduler. True or false?

• True
• False

Question 19: In the event of a failure, how can Spark recover a lost partition?

• Find the last good state in the RDD lineage and recompute the lost partition.
• Restart from the root RDD
• Find the last good state in the RDD lineage and recompute every task.
• Spark’s fail-safes ensure that failures will never occur.
• None of the above.

Question 20: Which of the following IDEs fully supports SBT?

• Eclipse
• IntelliJ
• Both Eclipse and IntelliJ
• None of the above

Enroll Here: Spark Fundamentals I Cognitive Class Exam Quiz Answers

Introduction to Spark Fundamentals I

“Spark Fundamentals I” typically refers to an introductory course or module that covers the basics of Apache Spark, a powerful open-source distributed computing system. Here’s a brief overview of what you might expect in such a course:

1. Introduction to Apache Spark: Understanding what Apache Spark is, its features, and its advantages over traditional MapReduce.
2. Spark Architecture: Overview of Spark’s architecture, including components like Driver, Executors, and Cluster Manager (e.g., YARN, Mesos).
3. Resilient Distributed Datasets (RDDs): RDDs are the fundamental data structure in Spark. Learning about RDDs, their operations (transformations and actions), and how they enable fault-tolerant distributed computations.
4. Spark SQL: Introduction to Spark’s SQL module for working with structured data, including DataFrame and Dataset APIs. Comparison with RDDs and benefits of using DataFrames.
5. Spark Streaming: Basic concepts of Spark Streaming for processing real-time data streams using DStreams and Structured Streaming.
6. Machine Learning with MLlib: Overview of MLlib, Spark’s scalable machine learning library, covering basic algorithms and data pipelines for machine learning tasks.
7. Graph Processing with GraphX: Introduction to GraphX for graph processing and analysis, covering basic graph algorithms and operations.
8. Deployment and Cluster Management: Basics of deploying Spark applications on clusters, configuring Spark properties, and managing resources effectively.
9. Performance Tuning: Techniques for optimizing Spark applications, including partitioning, caching, and choosing appropriate transformations and actions.
10. Integration with Other Systems: How Spark integrates with other big data systems like Hadoop, Hive, and external data sources.

When working with Spark, it’s crucial to understand these core concepts to leverage the framework effectively for distributed data processing, machine learning, and graph analytics. Additionally, consider exploring Spark’s documentation and examples to deepen your understanding and practical skills.

Spark Fundamentals I Cognitive Class Certification Answers

Module 1 – Introduction to Spark Quiz Answers

Question 1: What gives Spark its speed advantage for complex applications?

• Spark can cover a wide range of workloads under one system
• Various libraries provide Spark with additional functionality
• Spark extends the MapReduce model
• Spark makes extensive use of in-memory computations
• All of the above

Question 2: For what purpose would an Engineer use Spark? Select all that apply.

• Analyzing data to obtain insights
• Programming with Spark’s API
• Transforming data into a useable form for analysis
• Developing a data processing system
• Tuning an application for a business use case

Question 3: Which of the following statements are true of the Resilient Distributed Dataset (RDD)? Select all that apply.

• There are three types of RDD operations.
• RDDs allow Spark to reconstruct transformations
• RDDs only add a small amount of code due to tight integration
• RDD action operations do not return a value
• RDD is a distributed collection of elements parallelized across the cluster.

Module 2 – Resilient Distributed Dataset and Data Frames Quiz Answers

Question 1: Which of the following methods can be used to create a Resilient Distributed Dataset (RDD)? Select all that apply.

• Creating a directed acyclic graph (DAG)
• Parallelizing an existing Spark collection
• Referencing a Hadoop-supported dataset
• Using data that resides in Spark
• Transforming an existing RDD to form a new one

Question 2: What happens when an action is executed?

• The driver sends code to be executed on each block
• Executors prepare the data for operation in parallel
• A cache is created for storing partial results in memory
• Data is partitioned into different blocks across the cluster
• All of the above

Question 3: Which of the following statements is true of RDD persistence? Select all that apply.

• Persistence through caching provides fault tolerance
• Future actions can be performed significantly faster
• Each partition is replicated on two cluster nodes
• RDD persistence always improves space efficiency
• By default, objects that are too big for memory are stored on the disk

Module 3 – Spark Application Programming Quiz Answers

Question 1: What is Spark Context?

• A tool for linking to nodes
• A tool that provides fault tolerance
• A programming language for applications
• The built-in shell for the Spark engine
• An object that represents the connection to a Spark cluster

Question 2: Which of the following methods can be used to pass functions to Spark? Select all that apply.

• Transformations and actions
• Passing by reference
• Static methods in a global singleton
• Import statements
• Anonymous function syntax

Question 3: Which of the following is a main component of a Spark application’s source code?

• Import statements
• Business Logic
• Spark Context object
• Transformations and actions
• All of the above

Module 4 – Introduction to the Spark Libraries Quiz Answers

Question 1: Which of the following is NOT an example of a Spark library?

• MLlib
• Hive
• Spark SQL
• GraphX
• Spark Streaming

Question 2: From which of the following sources can Spark Streaming receive data? Select all that apply.

• Kafka
• JSON
• Parquet
• HDFS
• Hive

Question 3: In Spark Streaming, processing begins immediately when an element of the application is executed. True or false?

• True
• False

Module 5 – Spark Configuration, Monitoring and Turning Quiz Answers

Question 1: hich of the following is a main component of a Spark cluster? Select all that apply.

• Driver Program
• Spark Context
• Cluster Manager
• Worker Node
• Cache

Question 2: What are the main locations for Spark configuration? Select all that apply.

• The Spark Conf object
• The Spark Shell
• Executor Processes
• Environment variables
• Logging properties

Question 3: Which of the following techniques can improve Spark performance? Select all that apply.

• Scheduler Configuration
• Memory Tuning
• Data Serialization
• Using Broadcast variables
• Using nested structures

Spark Fundamentals I Final Exam Answers

Question 1: Which of the following is a type of Spark RDD operation? Select all that apply.

• Parallelization
• Action
• Persistence
• Transformation
• Evaluation

Question 2: Spark must be installed and run on top of a Hadoop cluster. True or false

• True
• False

Question 3: following operations will work improperly when using a Combiner?

• Average
• Maximum
• Minimum
• Count
• All of the above operations will work properly

Question 4: Spark supports which of the following libraries?

• Spark SQL
• MLlib
• GraphX
• Spark Streaming
• All of the above

Question 5: Spark supports which of the following programming languages?

• Scala, Perl, Java
• Scala, Java, C++, Python, Perl
• Scala, Python, Java, R
• Java and Scala
• C++ and Python

Question 6: A transformation is evaluated immediately. True or false?

• True
• False

Question 7: Which storage level does the cache() function use?

• MEMORY_ONLY
• MEMORY_ONLY_SER
• MEMORY_AND_DISK
• MEMORY_AND_DISK_SER

Question 8: Which of the following statements does NOT describe accumulators?

• They can only be added through an associative operation
• Programmers can extend them beyond numeric types
• They can only be read by the driver
• They are read-only
• They implement counters and sums

Question 9: You must explicitly initialize the Spark Context when creating a Spark application. True or false?

• True
• False

Question 10: The “local” parameter can be used to specify the number of cores to use for the application. True or false?

• True
• False

Question 11: Spark applications can ONLY be packaged using one, specific build tool. True or false?

• True
• False

Question 12: Which of the following parameters of the “spark-submit” script determine where the application will run?

• –master
• –conf
• –class
• –deploy-mode
• None of the above

Question 13: Which of the following is NOT supported as a cluster manager?

• Mesos
• Spark
• YARN
• Helix
• All of the above are supported

Question 14: Spark SQL allows relational queries to be expressed in which of the following?

• Scala, SQL, and HiveQL
• Scala and HiveQL
• Scala and SQL
• SQL only
• HiveQL only

Question 15:  Spark Streaming processes live streaming data in real-time. True or false?

• True
• False

Question 16: The MLlib library contains which of the following algorithms?

• Classification
• Regression
• Clustering
• Dimensionality Reduction
• All of the above

Question 17: What is the purpose of the GraphX library?

• To create a visual representation of the data
• To generate data-parallel models
• To create a visual representation of a directed acyclic graph (DAG)
• To perform graph-parallel computations
• To convert from data-parallel to graph-parallel algorithms

Question 18: Which list describes the correct order of precedence for Spark configuration, from highest to lowest?

• Flags passed to spark-submit, values in spark-defaults.conf, properties set on SparkConf
• Properties set on SparkConf, values in spark-defaults.conf, flags passed to spark-submit
• Values in spark-defaults.conf, properties set on SparkConf, flags passed to spark-submit
• Properties set on SparkConf, flags passed to spark-submit, values in spark-defaults.conf
• Values in spark-defaults.conf, flags passed to spark-submit, properties set on SparkConf

Question 19: Spark monitoring can be performed with external tools. True or false?

• True
• False

Question 20: Which serialization libraries are supported in Spark? Select all that apply.

• Apache Avro
• Java Serialization
• Protocol Buffers
• Kyro Serialization
• TPL

Enroll Here: Fundamentals of Encryption & Quantum-Safe Techniques Cognitive Class Exam Quiz Answers

Introduction to Fundamentals of Encryption & Quantum-Safe Techniques

Fundamentals of Encryption

Encryption is a process used to secure data by converting it into a form that can only be read or processed by authorized parties. The primary goal of encryption is confidentiality, ensuring that even if intercepted, the data remains unreadable to unauthorized users.

Key Concepts:

1. Encryption Algorithms: These are mathematical functions used to transform plaintext (original data) into ciphertext (encrypted data). Examples include AES (Advanced Encryption Standard), RSA (Rivest-Shamir-Adleman), and ECC (Elliptic Curve Cryptography).
2. Keys: Encryption algorithms use keys to encrypt and decrypt data. There are two main types of encryption:
   • Symmetric Encryption: Uses a single key for both encryption and decryption. Examples include AES.
   • Asymmetric Encryption: Uses a pair of keys (public and private keys). Data encrypted with the public key can only be decrypted with the corresponding private key. RSA and ECC are examples.
3. Use Cases: Encryption is used widely in securing communications (e.g., HTTPS for secure web browsing), protecting stored data (e.g., disk encryption), and ensuring the integrity of digital signatures (e.g., using digital certificates).

Quantum-Safe Techniques

With the emergence of quantum computers, traditional encryption methods such as RSA and ECC are at risk from quantum attacks. Quantum computers can solve certain mathematical problems much faster than classical computers, which could render current encryption vulnerable.

Quantum Threats to Encryption:

1. Shor’s Algorithm: A quantum algorithm that can efficiently factor large numbers, breaking RSA encryption and certain types of ECC used today.
2. Grover’s Algorithm: A quantum algorithm that can speed up the search of an unsorted database, effectively halving the security of symmetric key encryption like AES.

Quantum-Safe Encryption Techniques:

Researchers are actively developing quantum-resistant or quantum-safe encryption techniques to withstand attacks from quantum computers. These include:

1. Post-Quantum Cryptography (PQC): Algorithms that are believed to be secure against both classical and quantum computers. Examples include:
   • Lattice-based Cryptography
   • Hash-based Cryptography
   • Code-based Cryptography
   • Multivariate Cryptography
   • Isogeny-based Cryptography
2. Transition Planning: Organizations and standards bodies are working on protocols to transition from current cryptographic standards to quantum-safe alternatives, ensuring data protection in a future quantum computing era.

Conclusion:

Understanding the fundamentals of encryption is crucial for appreciating the challenges posed by quantum computing and the necessity for quantum-safe encryption techniques. As quantum computers advance, so too must our defenses against potential cryptographic vulnerabilities they may exploit.

Fundamentals of Encryption & Quantum-Safe Techniques Cognitive Class Certification Answers

Module 1: Introduction to Encryption Quiz Answers

Question 1: According to the referenced report, what is the average cost of a data breach?

• $300K
• $10M
• $1.87M
• $4.35M

Question 2: What type of encryption uses the same key to both encrypt and decrypt the data?

• Public key encryption
• Private key encryption
• Same key encryption
• Single key encryption

Question 3: In what year was 56bit DES encryption broken?

• 1998
• 1978
• 2000
• 1988

Question 4: Encrypted data is also known as what?

• Gibberish
• Encrypted text
• Cypher text
• Plain text

Question 5: Why did a South African bank have to replace 12 Million bank cards?

• An insider shared all of the card numbers and their associated PINs
• Hackers stole their PIN numbers
• Hackers stole the card numbers
• An insider gained access to the bank’s master encryption key

Module 2: The Quantum Era and Quantum Computers Quiz Answers

Question 1: What is the gold standard for public key encryption today?

• DES 512
• RSA 4096
• AES 256
• RSA 2048

Question 2: Which of the 4 finalist algorithms as selected by NIST is hash based?

• SPHINCS+
• CRYSTALS-Dilithium
• FALCON
• CRYSTALS-Kyber

Question 3: True or false, AES 128 is currently considered to be Quantum-safe?

• True
• False

Question 4: How does Grover’s algorithm impact the strength of an encryption key?

• It decreases it by 75%
• It doubles it
• It quadruples it
• It cuts it in half

Question 5: A qubit is like doing what to a coin?

• Cutting it in half
• Doubling it
• Spinning it
• Flipping it

Module 3: Safeguard your Data with Quantum-Safe Cryptography Quiz Answers

Question 1: Peak visibility is achieved by analyzing what?

• Logs and metrics
• Logs, metrics, and traces
• Metrics and traces
• Logs and traces

Question 2: Cryptographic agility requires which of the following?

• An architecture that can evolve over time
• The ability to suppprt both Quantum-safe algorithms and current algorithms
• A web service based architecture
• Quantum-safe algorithms

Question 3: Which of the following is NOT one of the ways to build a cryptographic inventory?

• Run time tracing
• Manual discovery
• Static scanning
• Extraction from key management tools

Question 4: What are the two types of data discovery?

• Manual and Application
• User and Automated
• Manual and Automated
• User and Application

Question 5: Y2Q is also known as what?

• Q-Day
• Quantum-safe
• Years before Quantum computers
• Years to Quantum computing

Fundamentals of Encryption & Quantum-Safe Techniques Quiz Answers

Question 1: Which of the following is NOT one of the steps in the encryption key management lifecycle?

• Rotation
• Reinstatement
• Creation
• Revocation

Question 2: The longer an encryption key is used, the more data it is likely protecting, which does what to the value of that key to attackers?

• Negates it
• Has no impact
• Increases it
• Decreases it

Question 3: What are the 3 main types of key management systems?

• Native, local, and global
• Localized, centralized, and decentralized
• Native, non-native, and global
• Native, localized, and centralized

Question 4: According to the referenced report, what is the average cost of a data breach?

• $4.35M
• $1.87M
• $300K
• $10M

Question 5: What can NOT happen if current cryptography (encryption) is broken?

• Data becomes more secure
• Change history
• Launch extortion attacks
• Create fake identities

Question 6: On January 15, 1977, the NBS adopted the first-ever Data Encryption Standard (DES) for the United States, and the world would soon follow. Who created this algorithm?

• Google
• Microsoft
• NIST
• IBM

Question 7: Digital signatures, like those used to sign legal documents online, are based on what?

• Least trust coordination
• Private keys
• Quantum-safe algorithms
• Public keys

Question 8: What does the HTTPS indicate in a website URL?

• The website name has been encrypted
• There are multiple websites with the same IP address
• There are multiple websites with the same URL
• The website is secured using SSL/TLS

Question 9: What is the gold standard for public key encryption today?

• DES 512
• RSA 4096
• AES 256
• RSA 2048

Question 10: Which of the 4 finalist algorithms does NIST recommend as the primary algorithm for digital signatures?

• FALCON
• CRYSTALS-Dilithium
• SPHINCS+
• CRYSTALS-Kyber

Question 11: Which of the 4 finalist algorithms as selected by NIST is the largest and slowest?

• FALCON
• CRYSTALS-Dilithium
• SPHINCS+
• CRYSTALS-Kyber

Question 12: Which algorithm can be used on a Quantum computer to crack today’s public key encryption?

• Pythagorean Theorem
• Mosca’s Theorem
• Shor’s Algorithm
• Grover’s Algorithm

Question 13: A 4 qubit machine can represent how many bit combinations at the same time?

• 16
• 4
• 1024
• 1

Question 14: According the Michele Mosca, what is the probability that public key encryption will be broken by the year 2031?

• 1 in 1000
• 1 in 5
• 1 in 7
• 1 in 2

Question 15: Today’s most widespread encryption protocols rely on which of the following to provide cybersecurity?

• VPNs
• The memory limits of classical computers
• Firewalls
• The computational limits of classical computers

Question 16: Which of the following is NOT one of the ways to build a cryptographic inventory?

• Run time tracing
• Manual discovery
• Static scanning
• Extraction from key management tools

Question 17: A DevSecOps model allows security teams to do what?

• Change data encryption throughout the software development lifecycle
• Implement and monitor data encryption throughout the software development lifecycle
• Monitor data encryption throughout the software development lifecycle
• Implement data encryption throughout the software development lifecycle

Question 18: What open source project provides a common API today for testing and prototyping?

• The Quantum Safe Security (QSS) project
• The Open Quantum Safe (OQS) project
• The Post Quantum Safe (PQS) project
• The Quantum Security (QS) project

Question 19: Which of the following is NOT part of a cryptographic inventory?

• Algorithm
• Key Length
• Owner
• Key

Question 20: Organizations can examine their hardware security modules to find encryption information. What approach to finding cryptography is this considered to be?

• Static scanning
• Run time tracing
• Automatic
• Manual

Question 21: In order to be valuable, a crptographic inventory needs to be ________.

• Compiled into the application
• Shared with the CSO
• Appended to the data catalog
• Kept up to date

Question 22: Which of the 4 milestones to becoming Quantum-safe can be started today?

• None
• Only Preparation
• Preparation, Discovery, and Transformation
• Preparation and Discovery

Question 23: As you add qubits to a Quantum computer, it’s power increases how?

• Incrementally
• Proportionally
• Fractionally
• Exponentially

Question 24: Which of the following is NOT one of the states that data can be in?

• In use
• At rest
• In motion
• On the cloud

Question 25: Which of the following is most well known for securing data at rest today?

• GDPR encryption
• RSA encryption
• SSL/TLS encryption
• AES encryption

Enroll Here: Docker Essentials: A Developer Introduction Cognitive Class Exam Quiz Answers

Introduction to Docker Essentials: A Developer Introduction

“Docker Essentials: A Developer Introduction” serves as a foundational guide to understanding Docker technology for developers. This introductory course explores Docker’s key concepts, benefits, and practical applications in modern software development environments.

In this course, developers will learn:

1. Introduction to Docker: An overview of what Docker is, its architecture, and how it revolutionizes application packaging and deployment.
2. Docker Installation and Setup: Step-by-step instructions on how to install Docker on various operating systems and set up the development environment.
3. Docker Images and Containers: Understanding Docker images, containers, and the Dockerfile for building custom images.
4. Managing Docker Containers: Techniques for managing containers, including starting, stopping, and interacting with containers.
5. Docker Networking: How Docker facilitates networking between containers and with the outside world.
6. Docker Volumes: Using Docker volumes to persist data and share data between containers.
7. Docker Compose: Introduction to Docker Compose for defining and managing multi-container Docker applications.
8. Best Practices and Tips: Best practices for Docker usage, security considerations, and troubleshooting common issues.

By the end of this course, developers will have a solid understanding of Docker fundamentals and be equipped to integrate Docker into their development workflow effectively. This knowledge will empower them to leverage Docker to streamline application development, deployment, and scalability processes.

Docker Essentials: A Developer Introduction Cognitive Class Certification Answers

Lab 1: Run your First Container Quiz Answers

Question 1: Containers achieve isolation because of what feature in the Linux kernel?

• Namespaces
• Memory Swap management
• SystemD init system
• Permissions mechanisms

Question 2: What is the difference between a Docker container and a Docker image?

• Nothing: these are two words for the same thing.
• An image is the blueprint for spinning up containers. An image is a TAR of a file system, and a container is a file system plus a set of processes running in isolation.
• Images are used to create the host machine that runs the Docker Engine.
• Images are used only as a backup and restore mechanism for containers.

Question 3: Control groups (cgroups) limit and monitor resources.

• True
• False

Question 4: Which statement is not true about Docker?

• Docker makes it easier to package applications and add to CI/CD pipelines.
• Docker helps you package dependencies with containers.
• Docker invented containers and Linux namespaces.
• Docker simplifies container technology to make creating and running containers easier.

Question 5: What tool makes it possible to run Docker containers on operating systems other than Linux?

• LinuxByte
• OSContainers
• Docker Swarm
• LinuxKit

Lab 2: Add CI/CD value with Docker Images Quiz Answers

Question 1: Which file should you use to create reproducible builds for Docker images?

• docker.yml
• docker.config
• Dockerfile
• README.md

Question 2: To rebuild and re-push images quickly, you should optimize your Dockerfile for what?

• Reproducibility: ensures your Dockerfile will create the same image every time it is built
• Documentation: helps other engineers build and push images faster
• Time to market: enables business users to get their features out faster
• The layer cache: put lines that change more frequently near the end of the file

Question 3: You must use Docker Hub as the central registry to share the Docker images that you create.

• True
• False

Question 4: What’s the purpose of the FROM line in a dockerfile? Select all that apply.

• It executes commands needed to set up your image for an application.
• It’s required as the first line in a Dockerfile.
• It copies a file into a directory.
• It specifies the starting image to build other image layers on top of.

Question 5: What does this command do: $ docker system prune.

• Stops and removes containers
• Stops running containers
• Retrieves a list of running containers
• Removes containers that are already stopped

Lab 3: Orchestrate applications with Docker Swarm Quiz Answers

Question 1: What is the operational model used by Docker Swarm for managing a cluster?

• Declarative
• Imperative
• Closed-source
• Non-existent

Question 2: What effect does the routing mesh have on a Docker swarm cluster?

• Commands, such as “docker service create,” that are sent to any node on the cluster will be routed to a manager that can respond to those commands.
• Requests that are sent to a published port on any node of the swarm will automatically be routed to a node that is running a container for that service.
• Every service that is created on the cluster will schedule at least one container for every node in the cluster to ensure global availability.
• You can enable the routing mesh to use layer-7 load balancing in front of your running containers.

Question 3: he “docker swarm init command” generates a join token. What is the purpose of that token?

• It allows you to remotely control production applications.
• It initializes a swarm.
• It makes sure that no malicious nodes join the swarm.
• It outputs nodes in a swarm.

Question 4: When you run the following command, which of the following events does not occur?

$ docker service update –replicas=5 –detach=true nginx1

nginx1

• The state of the service is updated to 5 replicas, which is stored in the swarm’s internal storage.
• Docker Swarm recognizes that the number of replicas that is scheduled now does not match the declared state of 5.
• This command checks aggregated logs on the updated replicas.
• Docker Swarm schedules 5 more tasks (containers) in an attempt to meet the declared state for the service.

Question 5: The more manager nodes you have, the easier it is to achieve a consensus on the state of a cluster.

• True
• False

Docker Essentials: A Developer Introduction Final Exam Answers

Question 1: What is one advantage of isolation that is provided by containers?

• Container isolation provides a mechanism for immutable infrastructure.
• Containers running in isolation means that you don’t have to deal with conflicting dependencies that are installed on the host or other containers.
• Isolated containers make it easier to monitor applications running inside of containers.
• Each isolated container provides you with an SSH server that can be used for debugging or automation purposes.

Question 2: What factors should you consider when you choose your FROM image?

• Size
• Security
• Source
• All of these

Question 3: What is the best way to distribute a Dockerized application to different environments?

• Maven Nexus repository
• Docker registry
• Source control
• Any of these

Question 4: What is a good starting point for the number of manager nodes in a cluster?

• 3
• 1
• 4
• > 10

Question 5: Running containerized applications in production can come with a variety of problems. Which of the following items is not a problem of running a containerized application in production?

• Scheduling containers across a distributed cluster
• High availability
• Scaling
• “Works on my machine” syndrome

Question 6: What is one advantage of using containers across different environments?

• Deploying containers only once, and Docker will deploy across all environments automatically
• Avoiding environmental drift caused by maintaining multiple environments
• Redeploying containers
• Creating independent applications

Question 7: You need an SSH server to get a bash shell inside a container.

• True
• False

Question 8: What technologies are behind Docker’s image layering system?

• Linux namespaces and control groups
• Storage driver plugins
• Union file system and copy-on-write
• Docker build and push caching

Question 9: What is the result of running the command “docker run ubuntu:15.04” 1000 times on your workstation?

• Your workstation will run 1000 Ubuntu containers and will likely handle this just fine.
• Your workstation will crash from being out of space if it’s running with 100 GB of storage or less.
• Your workstation will crash from being out of memory if it’s running with less than 4 GB of RAM.
• The developer-friendly interface of the Docker CLI will prevent you from doing something so silly.

Question 10: Open-source container orchestration platforms, such as Docker Swarm and Kubernetes, provide what to their users?

• A way to orchestrate different flavors of the same application in a way that maximizes reuse
• A fully hosted solution that is production-ready for deploying enterprise grade applications
• An easy-to-use GUI for operators to click and deploy applications
• A platform to help solve problems of running distributed containerized applications in production, such as high availability, scaling, fault tolerance, and scheduling

Enroll Here: Machine Learning with Python Cognitive Class Exam Quiz Answers

Introduction to Machine Learning with Python

“Introduction to Machine Learning with Python” is a comprehensive guide that introduces readers to the fundamental concepts and practical aspects of machine learning using Python programming language. Authored by Andreas C. Müller and Sarah Guido, this book is highly regarded for its clarity, depth, and hands-on approach to learning.

Key Topics Covered:

1. Fundamentals of Machine Learning:
   • Understanding what machine learning is and its applications.
   • Types of machine learning: supervised learning, unsupervised learning, and reinforcement learning.
2. Python for Machine Learning:
   • Introduction to essential Python libraries for machine learning: NumPy, pandas, matplotlib, and scikit-learn.
   • Hands-on examples and exercises using these libraries to implement machine learning algorithms.
3. Supervised Learning Algorithms:
   • Regression: Linear regression, polynomial regression.
   • Classification: Decision trees, random forests, support vector machines, k-nearest neighbors, and neural networks.
   • Evaluation metrics and cross-validation techniques.
4. Unsupervised Learning Algorithms:
   • Clustering: K-means clustering, hierarchical clustering.
   • Dimensionality reduction techniques: Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE).
5. Advanced Topics:
   • Feature engineering and feature selection.
   • Handling imbalanced datasets.
   • Introduction to deep learning and neural networks.
6. Practical Applications:
   • Real-world case studies and projects demonstrating the application of machine learning algorithms.
   • Best practices for model evaluation, tuning, and deployment.

Why Python?

Python has become the de facto language for machine learning due to its simplicity, readability, and the availability of powerful libraries such as NumPy, pandas, matplotlib, and scikit-learn. These libraries provide robust tools for data manipulation, visualization, and implementation of machine learning algorithms, making Python an ideal choice for both beginners and experienced practitioners.

Audience:

This book is suitable for:

• Beginners with no prior experience in machine learning who want to understand the basic concepts and practical implementation.
• Python programmers interested in expanding their skills to include machine learning techniques.
• Data scientists and researchers looking for a practical guide to apply machine learning algorithms using Python.

Conclusion:

“Introduction to Machine Learning with Python” serves as an excellent starting point for anyone interested in diving into the field of machine learning using Python. It balances theoretical concepts with hands-on exercises and real-world examples, making it a valuable resource for both learning and reference purposes.

Machine Learning with Python Cognitive Class Certification Answers

Module 1 – Machine Learning Quiz Answers

Question 1: Machine Learning uses algorithms that can learn from data without relying on explicitly programmed methods.

• True
• False

Question 2: Which are the two types of supervised learning techniques?

• Classification and Clustering
• Classification and K-Means
• Regression and Clustering
• Regression and Partitioning
• Classification and Regression

Question 3: Which of the following statements best describes the Python scikit library?

• A library for scientific and high-performance computation.
• A collection of algorithms and tools for machine learning.
• A popular plotting package that provides 2D plotting as well as 3D plotting.
• A library that provides high-performance, easy to use data structures.
• A collection of numerical algorithms and domain-specific toolboxes.

Module 2 – Regression Quiz Answers

Question 1: Training and testing on the same dataset might have a high training accuracy, but its out-of-sample accuracy might be low.

• True
• False

Question 2: If the correlation coefficient is 0.7 or lower, it may be appropriate to fit a non-linear regression.

• True
• False

Question 3: When we should use Multiple Linear Regression?

• When we would like to identify the strength of the effect that the independent variables have on a dependent variable.
• When there are multiple dependent variables.

Module 3 – Classification Quiz Answers

Question 1: In K-Nearest Neighbors, which of the following is true:

• A very high value of K (ex. K = 100) produces an overly generalised model, while a very low value of k (ex. k = 1) produces a highly complex model.
• A very high value of K (ex. K = 100) produces a model that is better than a very low value of K (ex. K = 1)
• A very high value of k (ex. k = 100) produces a highly complex model, while a very low value of K (ex. K = 1) produces an overly generalized model.

Question 2: A classifier with lower log loss has better accuracy.

• True
• False

Question 3: When building a decision tree, we want to split the nodes in a way that decreases entropy and increases information gain.

• True
• False

Module 4 – Clustering Quiz Answers

Question 1: Which one is NOT TRUE about k-means clustering??

• K-means divides the data into non-overlapping clusters without any cluster internal structure.
• The objective of k-means is to form clusters in such a way that similar samples go into a cluster and dissimilar samples fall into different clusters.
• As k-means is an iterative algorithm, it guarantees that it will always converge to the global optimum.

Question 2: Customer segmentation is a supervised way of clustering data based on the similarity of customers to each other.

• True
• False

Question 3: How is a center point (centroid) picked for each cluster in k-means?

• We can randomly choose some observations out of the dataset and use these observations as the initial means.
• We can select the centroid through correlation analysis.

Module 5 – Recommender Systems Quiz Answers

Question 1: Collaborative filtering is based on relationships between products and people’s rating patterns.

• True
• False

Question 2: Which one is TRUE about content-based recommendation systems?

• Content-based recommendation system tries to recommend items to the users based on their profile.
• In content-based approach, the recommendation process is based on similarity of users.
• In content-based recommender systems, similarity of users should be measured based on the similarity of the actions of users.

Question 3: Which one is correct about user-based and item-based collaborative filtering?

• In the item-based approach, the recommendation is based on the profile of a user that shows interest in a specific item.
• In the user-based approach, the recommendation is based on users of the same neighborhood, with whom he/she shares common preferences.

Machine Learning with Python Final Exam Answers

Question 1: You can define Jaccard as the size of the intersection divided by the size of the union of two label sets.

• True
• False

Question 2: When building a decision tree, we want to split the nodes in a way that increases entropy and decreases information gain.

• True
• False

Question 3: Which of the following statements are true? (Select all that apply.)

• K needs to be initialized in K-Nearest Neighbor.
• Supervised learning works on labelled data.
• A high value of K in KNN creates a model that is over-fit.
• KNN takes a bunch of unlabelled points and uses them to predict unknown points.
• Unsupervised learning works on unlabelled data.

Question 4: To calculate a model’s accuracy using the test set, you pass the test set to your model to predict the class labels, and then compare the predicted values with actual values.

• True
• False

Question 5: Which is the definition of entropy?

• The purity of each node in a decision tree.
• Information collected that can increase the level of certainty in a particular prediction.
• The information that is used to randomly select a subset of data.
• The amount of information disorder in the data.

Question 6: Which of the following is true about hierarchical linkages?

• Average linkage is the average distance of each point in one cluster to every point in another cluster.
• Complete linkage is the shortest distance between a point in two clusters.
• Centroid linkage is the distance between two randomly generated centroids in two clusters.
• Single linkage is the distance between any points in two clusters.

Question 7: The goal of regression is to build a model to accurately predict the continuous value of a dependent variable for an unknown case.

• True
• False

Question 8: Which of the following statements are true about linear regression? (Select all that apply)

• With linear regression, you can fit a line through the data.
• y=a+b_x1 is the equation for a straight line which can be used to predict the continuous value y.
• In y=θ^T.X, θ is the feature set and X is the “weight vector” or “confidences of the equation”, with both of these terms used interchangeably.

Question 9: The Sigmoid function is the main part of logistic regression, where Sigmoid of 0^T.X gives us the probability of a point belonging to a class, instead of the value of y directly.

• True
• False

Question 10: In comparison to supervised learning, unsupervised learning has:

• Less tests (evaluation approaches)
• More models
• A better, controlled environment
• More tests (evaluation approaches), but less models

Question 11: The points that are classified by Density-Based Clustering and do not belong to any cluster are outliers.

• True
• False

Question 12: Which of the following is false about Simple Linear Regression?

• It does not require tuning parameters.
• It is highly interpretable.
• It is fast.
• It is used for finding outliers.

Question 13: Which one of the following statements is the most accurate?

• Machine Learning is the branch of AI that covers the statistical and learning part of artificial intelligence.
• Deep Learning is a branch of Artificial Intelligence where computers learn by being explicitly programmed.
• Artificial Intelligence is a branch of Machine Learning that covers the statistical part of Deep Learning.
• Artificial Intelligence is the branch of Deep Learning that allows us to create models.

Question 14: Which of the following are types of supervised learning?

• Classification
• Regression
• KNN
• K-Means
• Clustering

Question 15: A bottom-up version of hierarchical clustering is known as divisive clustering. It is a more popular method than the Agglomerative method.

• True
• False

Question 16: Select all the true statements related to Hierarchical clustering and K-Means:

• Hierarchical clustering does not require the number of clusters to be specified.
• Hierarchical clustering always generates different clusters, whereas k-Means returns the same clusters each time it is run.
• K-Means is more efficient than Hierarchical clustering for large datasets.

Question 17: What is a content-based recommendation system?

• Content-based recommendation system tries to recommend items to the users based on their profile built upon their preferences and taste.
• Content-based recommendation system tries to recommend items based on similarity among items.
• Content-based recommendation system tries to recommend items based on the similarity of users when buying, watching, or enjoying something.

Question 18: Before running Agglomerative clustering, you need to compute a distance/proximity matrix, which is an n by n table of all distances between each data point in each cluster of your dataset.

• True
• False

Question 19: Which of the following statements are true about DBSCAN? (Select all that apply.)

• DBSCAN can be used when examining spatial data.
• DBSCAN can be applied to tasks with arbitrary shaped clusters, or clusters within clusters.
• DBSCAN is a hierarchical algorithm that finds core and border points.
• DBSCAN can find any arbitrary shaped cluster without getting affected by noise.

Question 20: In recommender systems, a “cold start” happens when you have a large dataset of users who have rated only a limited number of items.

• True
• False

Enroll Here: Using R with Databases Cognitive Class Exam Quiz Answers

Introduction to Using R with Databases

Using R with databases allows you to leverage the power of R for data analysis and manipulation directly with data stored in databases, without needing to first extract all the data into memory. This approach is beneficial for handling large datasets that exceed memory capacity, improving efficiency, and ensuring data integrity.

R provides several packages that facilitate interaction with databases, such as DBI (Database Interface) for connecting to databases, and specific packages like RMySQL, RPostgreSQL, ROracle, and RSQLite for connecting to MySQL, PostgreSQL, Oracle, and SQLite databases respectively.

The typical workflow for using R with databases involves:

1. Connecting to the Database: Establishing a connection to the database from R using appropriate credentials and connection parameters.
2. Querying the Database: Executing SQL queries directly on the database to retrieve data subsets or perform operations within the database environment.
3. Data Manipulation and Analysis: Applying R’s data manipulation and analysis capabilities to the data fetched from the database, such as summarizing data, creating visualizations, or fitting statistical models.
4. Closing the Connection: Closing the connection to the database once the operations are completed.

This integration allows you to combine the strengths of both R and databases, making it easier to work with large datasets efficiently and perform complex data analyses directly on the data where it resides.

Using R with Databases Cognitive Class Certification Answers

Module 1 – R and Relational Databases Quiz Answers

Question 1: What is the equivelent RDBMS concept for R dataframes?

• schema
• row (tuple)
• table (relation)
• column (attribute)
• database

Question 2: Is this statement true or false: Character data in R will be mapped to either a fixed sized CHARACTER column or a variable size VARCHAR column in a database.

• False
• True

Question 3: Select all the valid reasons for using R with relational databases:

• relational databases can manipulate large datasets
• R can retrieve dataframes that are stored in binary formats
• observations in dataframes can be changed dynamically by R
• relational databases provide concurrent access to data
• access to data can be managed using SQL GRANT and REVOKE statements

Module 2 – Connecting to Databases from R Quiz Answers

Question 1: Which statement best describes the odbcGetInfo() function:

• queries data from a relational database into an R dataframe
• provides similar information to the sqlColumns() function
• returns details about the database server you have established your connection with and ODBC conformance level
• limits the results to only certain types of tables or table object names
• all of the above

Question 2: True or False: ODBC consists of 2 components: an ODBC Driver Manager and one or more ODBC Drivers.

• False
• True

Question 3: Which of the following statements about RODBC are true (select all that apply)

• The odbcDataSources() function returns a character vector of DSNs.
• The odbcDriverConnect() function can be used to create a direct connection to a database without a registered Data Source Name or DSN.
• The sqlTables() function will return a dataframe of tables, views, or other table-like objects from the database server.
• A database connection can be established using the odbcConnect() or odbcDriverConnect() functions.
• The odbcConnect() function requires a special connection string that includes the database driver, database name and hostname.

Module 3 – Database Design and Analyzing Data Quiz Answers

Question 1: Which statement best describes the RODBC sqlFetch() function?

• returns all of the columns from a relational table
• returns all of the tables from a relational database
• returns all of the rows from a relational table
• returns all the data from a dataframe
• none of the above

Question 2: The LOAD command logs each transactions and stores the data very quickly on disk.

• False
• True

Question 3: Which functions can be used to create dataframes in R (select all that apply)

• sqlQuery()
• sqlClear()
• sqlCreate()
• sqlFetch()
• sqlUpdate()

Module 4 – Modifying Data and Using Stored Procedures Quiz Answers

Question 1: Which of the following statements is false?

• sqlSave() function can be used to append new rows to an existing table.
• SQL UPDATE statements and the sqlQuery() can be used to modify data in database tables.
• sqlUpdate() function can be used to insert new rows in a table.
• sqlSave() function can store the modified dataframe in a new database table.
• sqlUpdate() function can be used to push the updated data to existing rows in existing tables.

Question 2: True or False: The sqlCreate() function in RODBC can be used to create a table and store the data from the data frame into the newly created table with the same column names as the dataframe.

• False
• True

Question 3: Select all the statements that are true:

• The sqlQuery() function can be used to modify data in database table.
• The sqlSave() function stores the modified database data in an existing or new R dataframe.
• As an alternative, SQL UPDATE statements can be used to modify data in a dataframe.
• The update in place operation can be performed using the sqlUpdate() function in RODBC.
• The sqlUpdate() function can be used to push the updated data to the table where it originally came from.

Module 5 – In-Application Analytics with R Quiz Answers

Question 1: Which statement best describes in-database analytics?

• in-database analytics moves the data into a database before performing data analysis on it
• in-data base analytics requires ibmdbR and dashDB to perform data analysis
• in-database analytics processes the data and performing analysis within the database where the data is stored
• in-database analytics refers to data analysis on data that must be retrieved from a database
• in-database analytics retrieves data from a database and performs data analysis on private or public clouds

Question 2: Is this statement true or false: dashdb has built-in support for R execution without the use of built-in analytic functions and tools

• False
• True

Question 3: Which statements describe ibmdbR? (Select all that apply)

• ibmdbR provides methods to read, write, and sample data from a dashDB database
• ibmdbR provides access methods for in-database analytic functions and functions for storing R objects
• ibmdbR is hybrid data warehouse that can be deployed on public or private clouds for in-database analyltics
• ibmdbR is an R function for processing data and performing analysis
• ibmdbR is an open-source library for use in R runtimes such as Jupyter notebooks or R Studio

Using R with Databases Final Exam Answers

Question 1: Which R concept maps to an RDBMS database?

• table
• none
• column (attribute)
• row (tuple)
• schema

Question 2: R has difficulty with which of the following. Choose all that are applicable.

• manipulating large datasets
• performing data analysis
• performing data visualization
• providing concurrent access to data
• persistence, i.e., updating or changing observations within a data frame without first loading the data into R and then recreating the data file on disk

Question 3: Which R concept maps to an RDBMS row or tuple?

• table
• none
• observation
• variable
• schema

Question 4: True or False? RJDBC does not support substitution of arguments in SQL statements

• False
• True

Question 5: Which function returns platform details about numeric types and limits?

• odbcGetInfo()
• sqlTables()
• R .Machine
• sqlColumns()
• odbcDataSources()

Question 6: Which of the following statements are true? Choose all that are applicable.

• The saveRDS() and readRDS() functions can be used to store and retrieve a single R object, respectively
• The save.image() function preserves all of the images in your current R workspace.
• The save() function persists single objects in R dataframes.
• Character data in R will be mapped to either a fixed sized CHARACTER column or a variable size VARCHAR column in a database.
• R can retrieve dataframes that are stored in binary format

Question 7: Which function can be used to call stored procedures from R?

• sqlSelect()
• sqlRQuery()
• sqlQuery()
• sqlStoredProc()
• none of the above

Question 8: True or False? RJDBC is a package implementing a DBI in R on the basis of JDBC.

• False
• True

Question 9: To preserve the integrity of the data as it is analyzed using R, as the data is queried from a relational database into an R dataframe it is important to understand the data types used to store the data in the database. Which of the following commands, statements, or functions provides the column names, data types, precision/size, and whether or not null values are allowed? (select all that apply)

• DESCRIBE command
• odbcGetInfo() function
• odbcGetTypeInfo() function
• sqlColumns() function
• SQL SELECT statement

Question 10: True or False. There are optional parameters available with the sqlTables() function to limit the results to only certain types of tables or table object names can be limited using wildcard characters.

• False
• True

Question 11: Commonly used visualization for database modelling involves the use of:

• Logical models
• Physical models
• Entity- Relationship or ER diagrams
• Database schemas
• Flowchart diagrams

Question 12: Which statement is false?

• OUTPUT parameters cannot be returned from a stored procedure using RODBC, however result set can be returned.
• The sqlUpdate() function can be used to push the updated data to the R dataframe where it originally came from.
• Stored procedures are valuable tools for performing analysis on data that is stored in a relational database server and they can be used to dramatically reduce the time required to perform data intensive tasks as they execute on the database server itself.
• The sqlUpdate() function can be used to modify data in a database from R, even if not not recommended.
• It is recommended to set the errors parameter to false and check for a -1 return value to properly handle any errors.

Question 13: Which statements describe stored procedures? Choose all that are applicable.

• stored procedures are data-centric code modules that are stored and executed on the database server
• stored procedures must be coded only by experienced DBAs
• stored procedures do not allow dynamic parameters
• stored procedures can be created using one of many different stored procedure languages
• stored procedures eliminate network traffic delay because processing is performed on the database server

Question 14: True or False. A full-table select can be accomplished using the sqlFetch() function. The function will dynamically create a dataframe and populate it with the data from the database server.

• False
• True

Question 15: Which statements are true about in-database analytics? Choose all that are applicable.

• in-database analytics involves processing of data and performing analysis in the analytic application itself
• IBM dashDB supports in-database analytics with R
• In-database Analytics refers to processing the data and performing of analysis within the database itself, where data is stored
• ibmdbR is an open-source R library that facilitates in-database analytics from ibm databases like dashDB
• in-database analytics reduces network traffic and data movement because analytics processing is performed on the database server

Enroll Here: R for Data Science Cognitive Class Exam Quiz Answers

Introduction to R for Data Science

R is a powerful programming language and environment specifically designed for statistical computing and data analysis. It is widely used among statisticians and data miners for developing statistical software and performing data analysis. If you’re getting started with R for data science, here are some key points to understand:

1. Installation: You can download and install R from the Comprehensive R Archive Network (CRAN) website (cran.r-project.org). Additionally, you might want to use RStudio, which is a popular integrated development environment (IDE) for R that makes it easier to work with.
2. Basics of R: R is an interpreted language, which means you can interactively use it directly, or write scripts and functions. It has a rich set of built-in functions and libraries (called packages) that extend its functionality.
3. Data Types: R supports various data types including vectors, matrices, data frames, and lists. Understanding these structures is crucial for manipulating and analyzing data.
4. Data Manipulation: R provides powerful tools for data manipulation, transformation, and cleaning. Functions like subset(), merge(), aggregate(), and many others are commonly used.
5. Statistical Analysis: R is particularly strong in statistical modeling and analysis. You can perform regression analysis, hypothesis testing, clustering, time-series analysis, and more using built-in functions or packages like lm(), t.test(), kmeans(), arima().
6. Data Visualization: R offers excellent tools for data visualization through packages like ggplot2, which allows you to create highly customizable plots and graphs for exploratory data analysis and presentation.
7. Packages: R’s functionality can be extended by installing packages from CRAN or other repositories. These packages cover a wide range of topics including machine learning, text mining, spatial analysis, and more.
8. Reproducibility: R promotes reproducible research through its ability to create scripts and documents (using tools like R Markdown) that combine code, results, and explanatory text in a single document.
9. Community and Resources: R has a large and active community of users and developers. This means there are plenty of tutorials, forums, and online resources to help you learn and solve problems.
10. Learning Resources: To get started, consider resources like the official R documentation (r-project.org/documentation.html), online courses (such as those on Coursera or DataCamp), books like “R for Data Science” by Garrett Grolemund and Hadley Wickham, and exploring example code and scripts.

R’s flexibility and extensive libraries make it a preferred choice for many data scientists and statisticians. Whether you’re analyzing data, building models, or creating visualizations, R provides a comprehensive environment for all stages of the data science workflow.

R for Data Science Cognitive Class Certification Answers

Module 1 – R Basics Quiz Answers

Question 1: Vectors in R can be which of the following types?

• Logical
• Numeric
• Character
• All of the above

Question 2: What would be the output in R given: c(1,2) == 1 ?

• FALSE TRUE
• TRUE FALSE
• FALSE FALSE
• TRUE TRUE

Question 3: How would you retrieve the items larger than 5 (as in 15 and 10) from the following vector: costs <- c(3, 15, 3, 10)?

• costs[15,10]
• costs[c(15,10)]
• costs(costs > 5)
• costs[costs > 5]
• costs > 5

Module 2 – Data Structures in R Quiz Answers

Question 1: Give a 5 x 5 matrix object, movies, how would you retrieve the bottom-left item?

• movies[1,5]
• movies(5,5)
• movies[5,1]
• movies[5,5]
• movies[“bottom-left”]

Question 2: Below we create a list for a student and his info. Select all the correct options can we use to retrieve his courses? john <- list(“studentid“ = 9, “age” = 18, “courses” = c(“Data Science 101”, “Data Science Methodology”))

• john[“courses”]
• john[3]
• john$courses
• All the above options are correct

Question 3: Select the correct code from the following options which produces the following result?

• data.frame(“student” = c(“john”, “mary”), “id” = c(1, 2))
• array(“student” = c(“john”, “mary”), “id” = c(1, 2))
• data.frame(c(“john”, “mary”), c(1, 2))
• data.frame(student = c(john, mary), id = c(1, 2))
• list(“student” = c(“john”, “mary”), “id” = c(1, 2))

Module 3 – R Programming Fundamentals Quiz Answers

Question 1: What output will the following produce?

    chance_precipitation <- 0.80

if( chance_precipitation > 0.5 ) {

    print(“Bring an umbrella”) } else {

        print(“Don’t bring an umbrella”)}

• “Thunderstorm warning”
• “Don’t bring an umbrella”
• “Bring an umbrella”
• Some sort of error

Question 2: Which of the following statements are true?

• Using return() when writing a function is optional when you just want the result of the last line in the function to be the output of the function.
• Using return() when writing a function is necessary even when you just want the result of the last line in the function to be the output of the function.
• Using return() is useful when you want to produce outputs based on different conditions.
• Using return() serves no purpose when you want to produce outputs based on different conditions.

Question 3: Which of the following would you use to check the class of the object, myobject?

• class(myobject)
• type(myobject)
• class(object)
• class[myobject]

Module 4 – Working with Data in R Quiz Answers

Question 1: What does CSV stand for, when talking about tabular data files?

• Column-sorted values
• Comma-separated values
• Commonly-spaced values
• Column-separated values
• None of the above

Question 2: Which of the following are true?

• read.csv() can be used to read in CSV files
• You need to install libraries, such as the “readxl” library, to read Excel files into R
• You can load specified datasets or list the available datasets using data()
• You can write to a variety of filetypes, including .txt, .csv, .xls, .xlsx, and .Rdata.

Question 3: To get the number of characters in a character vector, char_vec, what function can you use?

• nchar(char_vec)
• numberOfCharacters[char_vec]
• char_vec.nchar()
• length(char_vec)

Module 5 – Strings and Dates in R Quiz Answers

Question 1: How would you combine the individual words from the vector, hw, into a single string, “Hello World”?

• hw <- c(“Hello”, “World”)
• paste(hw, collapse = ” “)
• paste(“Hello”, “World”)
• tolower(“Hello”, “World”)
• c(hw[1], hw[2])
• None of the above

Question 2: How would you convert the character string “2020-01-01” into a Date object in R?

• as.Date(“2020-01-01”)
• convertToDate(“2020-01-01”)
• date(“2020-01-01”)
• Sys.Date()

Question 3: What does the following regular expression pattern mean?

“.*@.+”

• Find matches containing an @ symbol where there is one or more characters before the @ symbol, and zero or more characters after the @ symbol.
• Find matches containing an @ symbol where there is one or more characters before the @ symbol, and at least one character after the @ symbol.
• Find matches containing an @ symbol where there is zero or more characters before the @ symbol, and at least one character after the @ symbol.
• It’s actually a new emoticon.

R for Data Science Final Exam Answers

Question 1: Which of the following will return TRUE?

• 1 > 2
• ”Apples” = “Bananas”
• TRUE = FALSE
• 2.1 in c(1.5, 3.14)
• None of the above

Question 2: Which of the following will print out the numbers 1, 2, and 3 only?

• for(num in c(1,2,3)) {print(num)}
• c(1,2,3)
• c(1:3)
• c(0,1,2,3,4,5)[2:4]

Question 3: How would you get the average of: ratings <- c(8.0, 8.5, 9.0)

• mean(“ratings”)
• AVER(“rating”)
• mean(ratings)
• average[ratings]

Question 4: How would you convert the following character vector into an integer vector?

• my_vector <- c(“1992”, “2016”, “2012”, “2018”)
• as.integer(my_vector)
• as.numeric(my_vector)
• tointeger(my_vector)
• converttointeger(my_vector)

Question 5: If you know an error might occur, what can you do?

• Think about why the error is happening and attempt to fix the code.
• Catch the error using the tryCatch() function.
• All of the above.
• Give up entirely.

Question 6: You have a file, “november.csv”, in the directory, “/Documents/expenses/“. How do you read this file into R?

• readLines(/Documents/expenses/november.csv)
• read.csv(“/Documents/expenses/november.csv”)
• read.csv(/Documents/expenses/november.csv)
• read.csv(“november.csv”, folder = “Documents/expenses”, type = “csv”)
• None of the above

Question 7: You opened a dataset and noticed a row showing Leonardo DiCaprio’s birthday as 153360000. What does it mean?

• 153360000 is a UNIX timestamp; it is the number of seconds since 1970-01-01 00:00:00.
• Leonardo DiCaprio was born on March 15, 1936 at 00:00.
• The data is definitely corrupt.
• Leonardo DiCaprio will be born in the year 153360000.

Question 8: The following code will produce which of the following outputs?

grep(“milk.+”, c(“cow’s milk”, “milkshake”, “milky”, “cat”, “milk1”, “milk”), value = T)

• “milkshake” “milky” “milk1”
• 2 3 5 6
• “milky” “milk1”
• “milkshake” “milky” “milk1” “milk”
• “cow’s milk” “milkshake” “milky” “cat” “milk1” “milk”

Question 9: You want to split a full name, “John Doe”, into a vector containing two elements: “John” and “Doe”. How would you do so?

• fullname <- “John Doe”
• unlist(strsplit(fullname, “ “))
• strsplit(fullname, “ “)
• None of the above.

Question 10: In R, x <- 1 is the same as x == 1

• True
• False

Question 11: Look at the code below. How many levels does the factor, drinks, have?

drinks <- factor(c(“tea”, “coffee”, “soft drink”, “tea”, “hot chocolate”, “hot chocolate”, “coffee”))

• 1
• 3
• 5
• 7
• None of the above

Question 12: To remove an existing column, “firstname”, from a data frame named “people”, which of the following code should you use?

• firstname <- NA
• people$firstname <- FALSE
• people(“firstname”) <- NA
• people$firstname <- NULL

Question 13: To retrieve the third row of an array named “myarray”, which of the following code should you use?

• myarray[3]
• myarray[,3]
• myarray(row = “third”)
• myarray[3,]

Question 14: How would you get the average of the third column of a data frame named “df”?

• mean(df[3,])
• mean(df[,3])
• mean(df[3])
• df[,3].mean()

Question 15: What is the expected output of the following script? 

myfunc <- function(x, y = 2){

x = x + 10

y = y + 100

return(y)

}

myfunc(3)

• 102
• 3
• 13
• 2