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Enroll Here: Deep Learning Fundamentals Cognitive Class Exam Quiz Answers

Introduction to Deep Learning Fundamentals

Deep learning is a subset of machine learning that deals with algorithms inspired by the structure and function of the brain’s neural networks. Here’s a concise introduction to its fundamentals:

1. Neural Networks: At the core of deep learning are artificial neural networks (ANNs), which are computational models inspired by the way biological neural networks in the human brain process information. ANNs consist of layers of interconnected nodes (neurons) that process input data and learn to recognize patterns.
2. Deep vs. Shallow Networks: Deep learning specifically refers to neural networks with multiple layers (deep architectures). Shallow networks, by contrast, have only a few layers or are single-layered (like a perceptron).
3. Learning from Data: Deep learning models learn to perform tasks by learning from large amounts of labeled data. This process is often referred to as training. The network adjusts its weights and biases based on the data it is exposed to, aiming to improve its performance on tasks such as classification, regression, or generation of new data.
4. Representation Learning: Deep learning excels in learning feature representations from raw data. This means that instead of handcrafting features for the model to use, deep learning algorithms can automatically learn relevant features from the input data.
5. Applications: Deep learning has shown remarkable success in various fields, including computer vision (e.g., image and video recognition), natural language processing (e.g., language translation, sentiment analysis), speech recognition, and reinforcement learning.
6. Key Algorithms: Popular deep learning algorithms include Convolutional Neural Networks (CNNs) for image-related tasks, Recurrent Neural Networks (RNNs) for sequential data, and Transformers for natural language processing tasks.
7. Challenges: Deep learning requires large amounts of data for training and considerable computational resources. Overfitting (where the model performs well on training data but poorly on new, unseen data) and interpretability of results are also significant challenges.
8. Frameworks and Tools: There are several popular frameworks (e.g., TensorFlow, PyTorch) and libraries (e.g., Keras) that facilitate the implementation and training of deep learning models, making the technology more accessible to developers and researchers.

Understanding these fundamentals provides a solid foundation for exploring deeper concepts and applications in the field of deep learning.

Deep Learning Fundamentals Cognitive Class Certification Answers

Module 1: Introduction to Deep Learning Quiz Answers

Question 1: Select the reason(s) for using a Deep Neural Network

• Some patterns are very complex and can’t be deciphered precisely by alternate means
• Deep Nets are great at recognizing patterns and using them as building blocks in deciphering inputs
• We finally have the technology – GPUs – to accelerate the training process by several folds of magnitude
• All of the above

Question 2: What is TRUE about the functions of a Multi-Layer Perceptron?

• The first neural nets that were born out of the need to address the inaccuracy of an early classifier, the perceptron.
• It predicts which group a given set of inputs falls into.
• It generates a score that determines the confidence level of the prediction.
• All of above.

Question 3: Why is the vanishing gradient a problem?

• Training is quick if the gradient is large and slow if its small
• With backprop, the gradient becomes smaller as it works back through the net
• The gradient is calculated multiplying two numbers between 0 and 1
• All of above.

Module 2: Deep Learning Models Quiz Answers

Question 1: For Unsupervised Learning, which of the following deep nets would you choose?

• Autoencoder or Restricted Boltzmann Machines
• Deep Belief Nets
• Convolutional Nets
• Recurrent Nets

Question 2: True or False: The RELU activation has no effect on back-propagation and the vanishing gradient

• True
• False

Question 3: True or False: Convolutional Nets are the right model when dealing with data that changes over time because of their built-in feedback loop, allowing them to serve as a forecasting engine.

• True
• False

Module 3: Additional Deep Learning Models Quiz Answers

Question 1: Which of the following are use cases of Deep nets?

• Sentiment Analysis of text data.
• Offering personalized ads based on user activity history.
• Flagging a transaction as fraudulent.
• Analyze and segment customers based on digital activity and footprint.
• Using satellite feeds and sensor data to detect changes in environmental conditions.
• All of the above.

Question 2: Which of the following are use cases of machine vision. Select all that apply.

• Image classification and tagging
• Sentiment Analysis
• Face Detection
• Video Recognition
• Speech Recognition

Question 3: Which of the following is a good application of an RNTN?

• If the patterns change through time
• For general classification problems
• If there is an unknown hierarchy inherent in the input features
• For Supervised Fine-tuning
• To determine the relative importance in the input features

Module 4: Deep Learning Platforms & Libraries Quiz Answers

Question 1: Which of the following is not an aspect of a deep net platform?

• Choice of deep net models
• Ability to integrate data from multiple sources
• Manage deep net models from the UI
• Under the hood performance enhancements to allow for fast training and execution
• Deriving the optimal hyper-parameter configuration

Question 2: What are the different aspects of a Deep Learning Library?

• They are a set of pre-built functions and modules that you can call through your own programs
• Usually maintained by high-performance teams and are regularly updated
• Most are open source and have a large community that contribute to the code base
• All of above.

Question 3: True or False: Theano, Caffe, and TensorFlow are examples of deep learning platforms.

• True
• False

Deep Learning Fundamentals Final Exam Answers

Question 1: For supervised learning, which of the following deep nets would you choose?

• Autoencoder
• Deep Belief Nets
• Convolutional Nets
• Restricted Boltzmann Machines
• Recurrent Nets

Question 2: Which of the following is true with respect to the training process of a deep net?

• The Cost is the difference between the net’s predicted and actual outputs.
• The training process utilizes gradients which measure the rate at which the weights and biases change with respect to the cost.
• The objective of the training process is to make the cost as low as possible.
• The training process utilizes a technique called back-propagation.
• All of above.

Question 3: True or False: With backprop, the early layers train slower than the later ones, making the early layers incapable of accurately identifying the pattern building blocks needed to decipher the full pattern.

• True
• False

Question 4: For image recognition, which of the following deep nets would you choose? Select all that apply.

• Autoencoder
• Deep Belief Nets
• Convolutional Nets
• Restricted Boltzmann Machines
• Recurrent Nets

Question 5: How does the Deep Belief Network (DBN) solve the vanishing gradient? Select all that apply.

• It uses a stack of RBMs to determine the initial weights and biases, where the output of any RBM forms the input to the next RBM.
• It uses a small labelled data set to associate patterns learned by the RBMs to classes.
• It utilizes supervised fine-tuning, resulting in tweaks in weights and biases and a slight improvement in accuracy.
• It quickly moves through solution states – set of weights and biases – going from one to another based on a reward.
• The complete process – RBMs for pre-training and supervised fine-tuning – results in a very accurate net which trains in an acceptable time.

Question 6: True or False: To train, a DBN combines two Learning methods – supervised and unsupervised.

• True
• False

Question 7: Which of the following is the most popular use of a Convolutional Net?

• Image Recognition
• Object Recognition in an Image
• Time Series Forecasting
• Supervised Fine Tuning
• General classification

Question 8: Which of the following are True about RBM? Select all that apply.

• The RBM is part of the first attempt at beating the vanishing gradient and uses unlabelled data.
• It improves its own accuracy through self-correction.
• Its purpose is to re-create inputs and in doing so has to make decisions about which input features are more important.
• It stores the relative importance of the features as weights and biases.
• It predicts which group a given set of inputs falls into.

Question 9: Which of the following statements are true about the architecture of a CNN? Select all that apply.

• A CNN can only have two types of layers: CONV and RELU.
• A RELU layer has to always be followed by a POOL layer.
• FC layers are usually found at the end.
• A CONV layer has a theoretical maximum number of filters.
• A typical CNN implementation has multiple repetitions of CONV, RELU and POOL layers, with sub-repetitions.

Question 10: True or False: By definition, the classifier in the nodes of an MLP cannot be anything other than the Perceptron.

• True
• False

Question 11: Which of the following are differences between a Recurrent Net and a Feedforward Net? Select all that apply.

• Recurrent Nets feed the output of any time step back in as input for the next step.
• Recurrent Nets are used for time series forecasting.
• Recurrent Nets can output a sequence of values.
• Recurrent Nets are trained using back-propagation.
• The nodes in recurrent nets have a classifier that activate and produce a score.

Question 12: Which of the following statements are true about training a Recurrent Net? Select all that apply.

• Since RNNs use backprop, the vanishing gradient is a problem.
• The number of time steps used for training has no bearing on the severity of the vanishing gradient problem.
• The vanishing gradient can potentially lead to decay of information through time.
• The most popular technique to address the vanishing gradient is the use of gates.
• The only technique to address the vanishing gradient is the use of gates.

Question 13: True or False: Deep Autoencoders are used for dimensionality reduction.

• True
• False

Question 14: Which of the following are true about Autoencoders? Select all that apply.

• It improves its own accuracy through self-correction.
• Its purpose is to re-create inputs and in doing so has to make decisions about which input features are more important.
• A Restricted Boltzmann Machine is a type of Autoencoders.
• It stores the relative importance of the features as weights and biases.
• It predicts which group a given set of inputs falls into.

Question 15: True or False: Given they are mainly about machine vision; Convolutional Nets don’t really find a home in the field of medicine.

• True
• False

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

Introduction to Machine Learning with R

Introduction to Machine Learning with R is a comprehensive guide that introduces readers to the fundamentals of machine learning using the R programming language. In this book, readers will learn how to implement various machine learning algorithms and techniques using practical examples and hands-on exercises.

Key topics covered in “Introduction to Machine Learning with R” typically include:

1. Introduction to Machine Learning: Basic concepts and principles of machine learning, including supervised and unsupervised learning, model evaluation, and feature selection.
2. R Programming: Essential R programming skills needed for machine learning, such as data manipulation, visualization, and working with packages like tidyverse and caret.
3. Supervised Learning Algorithms: Implementation of popular supervised learning algorithms such as linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), and k-nearest neighbors (kNN).
4. Unsupervised Learning Algorithms: Techniques for unsupervised learning tasks, including clustering (k-means, hierarchical clustering) and dimensionality reduction (principal component analysis, t-SNE).
5. Model Evaluation and Validation: Methods for evaluating and validating machine learning models, such as cross-validation, ROC curves, and precision-recall curves.
6. Feature Selection and Engineering: Techniques for selecting relevant features and engineering new features from existing data.
7. Advanced Topics: Depending on the book, it may cover advanced topics like ensemble methods, deep learning with R (using packages like keras or tensorflow), and natural language processing (NLP) applications.
8. Case Studies and Projects: Practical examples and case studies to demonstrate how to apply machine learning techniques to real-world datasets.

Overall, “Introduction to Machine Learning with R” aims to provide readers with a solid foundation in both machine learning concepts and practical skills using R. It’s suitable for beginners who are new to machine learning as well as more experienced practitioners looking to expand their knowledge of applying machine learning techniques using R.

Machine Learning with R Cognitive Class Certification Answers

Module 1 – Machine Learning with Statistical Modeling Quiz Answers

Question 1: Machine Learning was developed shortly (within the same century) as statistical modelling, therefore adopting many of its practices.

• True
• False

Question 2: Supervised learning deals with unlabeled data, while unsupervised learning deals with labelled data.

• True
• False

Question 3: Machine Learning is applied in current technologies, such as:

• Trend Prediction (ex. House Price Trends)
• Gesture Recognition (ex. Xbox Connect)
• Facial Recognition (ex. Snapchat)
• A and B, but not C
• All of the above

Module 2 – Supervised Learning I Quiz Answers

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

• 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 an overly generalised model, while a very low value of k (ex. k = 1) produces a highly complex model.
• A very low value of K (ex. K = 1) produces an overly generalised model, while a very high value of k (ex. k = 100) produces a highly complex model.
• All of the Above

Question 2: A difficulty that arises from trying to classify out-of-sample data is that the actual classification may not be known, therefore making it hard to produce an accurate result.

• 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 3 – Supervised Learning II Quiz Answers

Question 1: Which of the following is generally true about the evaluation models: Train and Test on the Same Dataset and Train/Test Split.

• Train and Test on the Same Dataset has a high training accuracy and high out-of-sample accuracy, while Train/Test Split has a low training accuracy and low out-of-sample accuracy.
• Train and Test on the Same Dataset has a low training accuracy and high out-of-sample accuracy, while Train/Test Split has a high training accuracy and low out-of-sample accuracy.
• Train and Test on the Same Dataset has a high training accuracy and low out-of-sample accuracy, while Train/Test Split has a low training accuracy and high out-of-sample accuracy.
• Train and Test on the Same Dataset has a low training accuracy and low out-of-sample accuracy, while Train/Test Split has a high training accuracy and high out-of-sample accuracy.

Question 2: Which of the following is true about bias and variance?

• Having a high bias underfits the data and produces a model that is overly complex, while having high variance overfits the data and produces a model that is overly generalized.
• Having a high bias underfits the data and produces a model that is overly generalized, while having high variance overfits the data and produces a model that is overly complex.
• Having a high bias overfits the data and produces a model that is overly complex, while having high variance underfits the data and produces a model that is overly generalized.
• Having a high bias overfits the data and produces a model that is overly generalized, while having high variance underfits the data and produces a model that is overly complex.

Question 3: Root Mean Squared Error is the most popular evaluation metric out of the three discussed, because it produces the same units as the response vector, making it easy to relate information.

• True
• False

Module 4 – Unsupervised Learning Quiz Answers

Question 1: What are some disadvantages that K-means clustering presents?

• Updating can occur even though there is a possibility of a centroid not having data points in its group
• K-means clustering is generally slower, compared to many other clustering algorithms
• There is high bias in the models, due to where the centroids are initiated
• None of the above

Question 2: Decision Trees tend to have high bias and low variance, which Random Forests fix.

• True
• False

Question 3: A Dendrogram can only be read for Agglomerative Hierarchical Clustering, not Divisive Hierarchical Clustering.

• True
• False

Module 5- Dimensionally Reduction & Collabrative Filtering Quiz Answers

Question 1: Filters produce a feature set that does not contain assumptions based on the predictive model, making it a useful tool to expose relationships between features.

• True
• False

Question 2: Principle Components Analysis retains all information during the projection process of higher order features to lower orders.

• True
• False

Question 3: Which of the following is not a challenge to a recommendation system that uses collaborative filtering?

• Diversity Sheep
• Shilling Attacks
• Scalability
• Synonyms

Machine Learning with R Final Exam Answers

Question 1: Randomness is important in Random Forests because it allows us to have distinct, different trees that are based off of different data.

• 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 is true?

• A high value of K in KNN creates a model with low bias and high variance
• An observation must contain values for all features
• A categorical value cannot be numeric
• None of the above

Question 4: In terms of Bias and Variance, Variance is the inconsistency of a model due to small changes in the dataset.

• True
• False

Question 5: Which is the definition of entropy?

• The purity of each node in a random forest.
• 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: In terms of Bias and Variance, Variance is the inconsistency of a model due to small changes in the dataset.

• True
• False

Question 8: Which is true about bootstrapping?

• All data points must be used when bootstrapping is applied
• The data points are randomly selected with replacement
• The data points are randomly selected without replacement
• It is the same as bagging

Question 9: Machine Learning is still in early development and does not have much of an impact on the current society.

• True
• False

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

• Less tests
• More models
• A better controlled environment
• More tests, but less models

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

• True
• False

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

• It does not require tuning parameters
• It is highly interpretable
• It is fast
• It has a low variability on predictive accuracy

Question 13: Machine Learning uses algorithms that can learn from data without relying on standard programming practices.

• True
• False

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

• Clustering
• Regression
• Classification
• Both A and B

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: Which is NOT a specific outcome of how Dimensionality Performance improves production?

• Highlights the main linear technique called Principle Components Analysis.
• Creates step-wise regression.
• Reduces number of features to be considered.
• Highlights relevant variables only and omits irrelevant ones.

Question 17: Feature Selection is the process of selecting the variables that will be projected from a high-order dimension to a lower one.

• True
• False

Question 18: Hierarchical Clustering is one of the three main algorithms for clustering along with K-Means and Density Based Clustering.

• True
• False

Question 19: Which one is NOT a feature of Dimensionality Reduction?

• It can be divided into two subcategories called Feature Selection and Feature Extraction
• Removal of an “outsider” from the least cohesive cluster.
• Feature Selection includes Wrappers, Filters, and Embedded.
• Feature Extraction includes Principle Components Analysis.
• It reduces the number of variables/features in review.

Question 20: Low bias tends to create overly generalized models, which can cause a loss of relevant relations between the features and target output. When a model has low bias, we say that it “under fits” the data.

• True
• False

Enroll Here: Introduction to Cloud Cognitive Class Exam Quiz Answers

Introduction to Cloud

Cloud computing is a transformative technology that has revolutionized how businesses and individuals use and manage computing resources. Cloud computing refers to the delivery of computing services—including servers, storage, databases, networking, software, and more—over the internet (“the cloud”). Instead of owning their own computing infrastructure or data centers, companies can rent access to anything from applications to storage from a cloud service provider. Here’s an introduction to the key concepts:

Key Characteristics:

1. On-demand self-service: Users can provision and manage computing resources as needed, without human intervention from the service provider.
2. Broad network access: Services are available over the network and can be accessed through standard mechanisms, promoting ubiquitous access.
3. Resource pooling: Resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to demand.
4. Rapid elasticity: Capabilities can be rapidly and elastically provisioned and released to scale rapidly outward and inward with demand.
5. Measured service: Cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts).

Service Models:

1. Infrastructure as a Service (IaaS): Provides virtualized computing resources over the internet. Users can rent virtual machines, storage, and other fundamental computing resources.
2. Platform as a Service (PaaS): Provides a platform allowing customers to develop, run, and manage applications without the complexity of building and maintaining the infrastructure typically associated with developing and launching an app.
3. Software as a Service (SaaS): Delivers software applications over the internet on a subscription basis. Users can access the software through a web browser without worrying about hardware or software maintenance.

Deployment Models:

1. Public Cloud: Owned and operated by third-party cloud service providers. Services are delivered over the public internet and shared across multiple organizations.
2. Private Cloud: Operated solely for a single organization. It can be managed internally or by a third-party and can be located on-premises or off-premises.
3. Hybrid Cloud: Combines public and private clouds, allowing data and applications to be shared between them.

Benefits:

• Cost-efficiency: No need to invest in and maintain physical infrastructure.
• Scalability: Easily scale resources up or down based on demand.
• Flexibility and Accessibility: Accessible from anywhere with an internet connection.
• Reliability and Disaster Recovery: Providers often offer redundancy and backup solutions.

Challenges:

• Security and Privacy: Concerns over data breaches and compliance with regulations.
• Integration Complexity: Integrating cloud services with existing IT infrastructure can be challenging.
• Downtime and Service Outages: Reliability of cloud services may vary based on provider and service level agreements.

Cloud computing has become a cornerstone of modern IT infrastructure, enabling innovation, flexibility, and cost-effectiveness across industries.

Introduction to Cloud Cognitive Class Certification Answers

Module 1 – Overview of Cloud Computing Quiz Answers

Question 1: In the US National Institute of Standards and Technology, NIST, definition of “cloud computing”, what does the statement “shared pool of configurable computing resources” include?

• Leverage cloud services over the open internet on hardware owned by the cloud provider
• Five essential characteristics, three deployment models, three service models
• Data security, associated with loss or unavailability of data causing business disruption
• Networks, servers, storage, applications, and services

Question 2: What tasks do hypervisors accomplish? Select two.

Note: Make sure you select all of the correct options!

• Enable multiple operating systems to run alongside each other, sharing the same physical computing resources.
• Facilitate access to mainframes for multiple users to access the same data storage layer.
• Scale on demand to support fluctuating workloads.
• Separates VMs logically and assigns each a share of the underlying computing power, memory, and storage.

Question 3: What are some of the cloud benefits that make it lower risk for enterprises to adopt cloud? Select two.

Note: Make sure you select all of the correct options!

• Data security associated with loss or unavailability of data causing business disruption
• The pay-as-you-go model allows enterprises to experiment with technologies as opposed to making long-term decisions based on little or no trial.
• Diversity of standardization in how the constantly evolving technologies integrate and interoperate.
• The speed with which applications can be up and running on the cloud versus months on traditional platforms, means enterprises can experiment, fail fast, learn, and course correct without setting them back significantly.

Module 2 – Cloud Adoption and Emerging Technologies Quiz Answers

Question 1: An IBM Institute for Business Value study says that more than three-quarters of enterprises today are using cloud computing to expand into new industries. What additional benefits do organizations find when adopting the cloud:

• Avoid having to fail at all
• Continue making expensive decisions because it often worked in the past
• Lengthen product lifecycles to ensure higher quality offerings
• Improve customer experience and create enhanced products and services

Question 2: What is the three-way symbiotic relationship between IoT, AI, and Cloud?

• AI consumes the data produced by IoT devices
• Making sense of the endless streams of data from IoT devices
• IoT delivers the data, AI powers the insights, and both emerging technologies leverage cloud’s scalability and processing power
• Power, scale, dynamic nature, and economics of the cloud resources

Question 3: What is the three-way relationship between blockchain, AI, and the Cloud?

• Lends trust and transparency to AI by recording the data and variables that go into a decision made in an AI algorithm.
• An immutable network allowing members to view only those transactions that are relevant to them.
• Globally distributed, scalable, and cost-efficient computing resources.
• Blockchain provides the trusted, decentralized source of truth, AI powers the analytics and decisions made from the collected data, and cloud provides the globally distributed, scalable, and cost-efficient computing resources to support both technologies.

Module 3 – Cloud Computing Service and Deployment Models Quiz Answers

Question 1: Which are some of the key components of cloud infrastructure? Select two.

Note: Make sure you select all of the correct options.

• IaaS providers manage the hypervisors and provision virtual instances
• IaaS providers deploy the middleware and install required applications on the virtual machines
• IaaS providers manage large data centers that contain the physical machines required to power the various layers of abstraction on top of them
• IaaS providers manage the hypervisors and end-users provision the virtual instances

Question 2: According to the video, which of these statements are true of Virtual Private Clouds (VPCs)? Select two.

Note: Make sure you select all of the correct options.

• VPCs are provisioned in a logically isolated part of a shared public cloud
• The VPC infrastructure is owned, managed, and operated by the public cloud service provider
• VPCs may be provisioned over a cloud service provider’s infrastructure, but the infrastructure is owned, managed, and operated by the enterprise
• VPC allows all users of the shared public cloud to access resources in the VPC on need basis; it also allows users of the VPC to access resources of the public cloud

Question 3: If an organization needs to run a highly sensitive and mission critical application with unpredictable performance and capacity requirements, which of the following models would best meet its requirements?

• Public Cloud
• Private Cloud
• On-premise Data Centers
• Hybrid Cloud

Module 4 – Components of Cloud Computing Quiz Answers

Question 1: Is it possible to run completely different operating systems on Virtual Machines (VMs) that are on a single host? If yes, what makes this possible?

• Yes, Virtualization makes it possible— to have VMs, running different operating systems, on a single host.
• No, it is not possible—A single host can only work as one single virtual environment, and can, therefore, have only one environment.
• Yes, Containerization makes it possible—to have VMs that are unique physical entities, so you can have completely different Operating Systems on them.
• No, it is not possible—VMs are software-based versions of a single host. They cannot have different environments from one another.

Question 2: Which of these scenarios is ideal for the use of bare metal servers, as opposed to virtual servers? Select two.

Note: Make sure you select all of the correct options!

• CPU and I/O intensive workloads
• Low cost to use
• Huge performance and strict security and compliance requirements
• Workloads that require limited throughput and performance

Question 3: Subnets are the main area where security is implemented in the cloud. What is used to provide security at the virtual instance level in subnets?

• Virtual Private Cloud or VPC
• Security Groups or SGs
• Access Control Lists or ACLs
• Load Balancers

Module 5 – Cloud Computing Storage and Content Delivery Networks Quiz Answers

Question 1: Which of these are features of Object Storage? Select two.

Note: Make sure you select all of the correct options!

• Storage is attached to compute nodes using a fibre network
• You can directly use Object Storage without attaching it to a compute node
• Object Storage can be mounted on multiple compute nodes via an ethernet network
• Object storage is effectively infinite

Question 2: There are four types of cloud storage available. Which storage type is ephemeral or non-persistent?

• File storage
• Direct Attached storage
• Object storage
• Block storage

Question 3: What is the main benefit of a Content Delivery Network (CDN)?

• Distribute tasks, workloads, and network traffic
• Enhanced control over network traffic by routing traffic to specified resources
• Speed, it makes your website faster by serving files from a location closer to the user.
• Network topology

Module 6 – Emergent Trends, Cloud Native, DevOps and Application Modernization Quiz Answers

Question 1: What are the key elements of a hybrid Multicloud strategy?

• For seamless working, it is recommended that if you’re subscribed to the infrastructure services of a cloud provider, you should subscribe to the application services provided by the same vendor.
• Embraces a mix of cloud models and services as long as they are from the same cloud service provider
• Embraces a mix of cloud models while creating a vendor lock-in with the cloud provider
• Connects an organization’s on-premise private cloud and third-party public cloud into a single infrastructure

Question 2: What is an attribute that distinguishes serverless computing from other compute models?

• End users pay for resources as long as they are running, even if idle
• In the serverless computing environment, compute resources cannot be scaled up or down
• The serverless model requires NO provisioning of servers, installation of application stacks, or operation of the infrastructure BY the users/developers
• Serverless computing does not require any underlying servers for executing workloads

Question 3: Cloud adoption is an integral part of application modernization. What are the other two important components of modernization?

• Microservices and DevOps
• Serverless and Virtual Machines
• Service Oriented Architecture and Waterfall Methodology
• VMs and Agile Methodology

Introduction to Cloud Final Exam Answers

Question 1: Which cloud deployment model lets users use multiple cloud models working together seamlessly?

• Broad Network Access
• Private
• Hybrid
• Public

Question 2: Which of these are essential characteristics of the Cloud? Select two.

Note: Make sure you select all of the correct options!

• Single-tenant
• On-demand self-service
• Resource pooling
• Fixed fee

Question 3: What are some cloud computing benefits? Select two.

Note: Make sure you select all of the correct options!

• Purchasing off-the-shelf software and upgrades
• Diversity of standardization in how the constantly evolving technologies integrate and interoperate.
• Competitive advantage
• Flexibility and efficiency

Question 4: According to the International Data Corporation (IDC), what is that crucial ability that will make cloud computing essential for businesses to succeed, sustain, and compete in today’s markets?

• Data-driven decisions
• Cognitively-enabled workflows
• Multi-cloud infrastructures
• Applied exponential technologies such as AI, Automation, IoT, and Blockchain

Question 5: IoT, AI, Blockchain, and Analytics are emerging technologies enabled by the cloud. What are some of the attributes of cloud computing that enable these technologies? Select two.

Note: Make sure you select all of the correct options!

• Cloud resources are offered in a single-tenant model
• The power and scale of cloud resources
• Computing resources can be accessed via internet connection
• Cloud offers on-demand computing

Question 6: In the car analogy used to explain the differences between the three cloud service models, which of the provided options is an analogy for PaaS?

• Renting a car
• Leasing a car
• Hiring a taxi
• Buying a car

Question 7: Which of these scenarios are good use cases for Public Cloud? Select two.

Note: Make sure you select all of the correct options!

• Reducing time-to-market for products and services
• Managing business critical platforms and applications
• Disaster recovery, data protection, and business continuity
• Applications with stable capacity and resourcing needs

Question 8: Private cloud platforms can be implemented internally or externally. What is an example of external private cloud platform?

• Controlled access and customized security measures
• Cloud platform that runs on-premises
• Platform that is owned, managed, and operated by the organization
• Virtual Private Cloud or VPC

Question 9: The infrastructure layer is the foundation of the cloud and consists of physical resources. Where do these physical resources reside?

• Compute, Storage, and Networking Resources
• In a warehouse that houses a centralized mainframe server
• Regions, Zones, and Data Centers
• Virtual Servers, Bare Metal Servers, and Serverless computing resources

Question 10: Why do bare metal servers take longer to provision than virtual servers?

• GPUs for data analytics are hard to configure and provision
• They’re multi-tenant servers, and all tenants must help configure them
• Customer configures the physical servers
• They are physical servers that can be customized to specified capacity

Question 11: Containers provide isolation of applications. What is one of the benefits this provides?

• Repackaged into containers or containerized microservices
• Help keep malicious code in one container from impacting other containers
• Open sourced runtime engine
• Abstraction from the host operating system

Question 12: What makes File storage an ideal solution for scenarios where common storage (a landing zone for incoming files) is needed?

• File Storage is mounted to compute nodes via an ethernet network
• Mounted from remote storage appliances
• Its consistently high speed
• File storage can be mounted to multiple compute nodes at a time

Question 13: Which of these scenarios are best suited for Block Storage? Select two.

Note: Make sure you select all of the correct options!

• Workloads that need low-latency storage
• Workloads that need disk sharing between compute nodes
• Low cost is a consideration
• Applications that need consistent fast access to disk, such as databases

Question 14: Which of the following is NOT a good fit for Object Storage?

• Data that does not require fast read and write speeds
• Files or objects that are static
• Operating systems or applications like databases
• Storage needs are finite and can be pre-defined

Question 15: What is a Content Delivery Network (CDN)?

• Network Topology
• A distributed Server Network
• A Network Link
• A Network Node

Question 16: What are some of the benefits of using microservices architecture? Select two.

Note: Make sure you select all of the correct options!

• Application components can be developed and updated independently of each other
• Each microservice of an application needs to use the same stack and runtime environment
• Components facing varied amounts of load can be scaled independently
• Each line of code for a microservice needs to be written from scratch

Question 17: What are the characteristics of a cloud native application?

• Collection of microservices that are built as one huge piece of software
• Collection of microservices with tightly coupled UI, business logic layer, and data layer
• Collection of microservices working together as a whole to comprise an application
• Collection of microservices that need to be scaled and updated in relation to each other

Question 18: DevOps’ tools, practices, and processes are helping tackle some of the complexities and challenges posed by the cloud. Identify two ways in which DevOps is mitigating these challenges. Select two.

Note: Make sure you select all of the correct options!

• DevOps processes outline the development principles that need to be followed to modernize monolithic applications to cloud native applications
• By creating an automated deployment pipeline
• By fully automating the infrastructure installation process in a way that is documented, repeatable, verifiable, and traceable
• DevOps best practices eliminate the need to provision servers, build middleware, and install application code

Enroll Here: Data Privacy Fundamentals Cognitive Class Exam Quiz Answers

Introduction to Data Privacy Fundamentals

Data privacy fundamentals are crucial principles that govern how sensitive information should be handled, stored, and shared to ensure individuals’ rights are respected. Here’s an introductory overview:

1. Definition of Data Privacy: Data privacy refers to the protection of personal data, ensuring that individuals’ information is not misused, accessed without authorization, or distributed without consent.
2. Key Concepts:
   • Personal Data: Information that can identify a specific individual (e.g., name, address, email, ID numbers).
   • Sensitive Data: Includes health information, financial details, racial or ethnic origin, political opinions, religious beliefs, etc.
   • Anonymization: Removing identifying information from data sets to protect privacy.
   • Consent: Permission given by individuals for the collection, use, and sharing of their personal data.
   • Data Minimization: Collecting only the necessary data for a specific purpose and limiting access to it.
3. Importance of Data Privacy:
   • Trust: Maintains trust between individuals and organizations.
   • Legal Compliance: Ensures compliance with data protection laws and regulations (e.g., GDPR, CCPA).
   • Risk Management: Reduces the risk of data breaches and unauthorized access.
   • Ethical Responsibility: Respects individuals’ rights and freedoms.
4. Data Privacy Principles:
   • Transparency: Informing individuals about how their data will be used.
   • Purpose Limitation: Collecting data only for specified, legitimate purposes.
   • Data Minimization: Limiting the collection of personal data to what is necessary.
   • Accuracy: Ensuring that personal data is accurate and up to date.
   • Security: Protecting personal data through appropriate technical and organizational measures.
   • Accountability: Being responsible for complying with data protection principles and demonstrating compliance.
5. Challenges and Considerations:
   • Technological Advances: New technologies may introduce risks to data privacy.
   • Globalization: Ensuring compliance with varying international data protection laws.
   • Data Breaches: Managing and mitigating the impact of data breaches on individuals.
6. Future Trends:
   • AI and Machine Learning: Implications for data privacy with the use of AI algorithms.
   • Blockchain: Potential for enhancing data privacy and security through decentralized systems.
   • Regulatory Developments: Continued evolution of laws and regulations to address emerging challenges.

Understanding these fundamentals is essential for organizations and individuals to navigate the complex landscape of data privacy, ensuring that personal information is handled responsibly and ethically.

Data Privacy Fundamentals Cognitive Class Certification Answers

Module 1 – Students Loans Data Breach (Canada) Quiz Answers

Question 1: What does PIPEDA stand for?

• Personal Incidents of Privacy for Electronic Documents Act
• Personal Information Protection and Electronic Documents Act
• Privacy Information of Protections of Electronic Documents Act
• Privacy Institute of Protections of Electronic Documents Act
• Privacy Initiative for Protection of Electronic Data Act

Question 2: Which data privacy federal law does the case study incident fall under in Canada?

• The Private Citizens Act
• The Privacy Act
• The Personal Information Protection and Electronic Data Act (PIPEDA)
• The Personal Information Protection and Electronic Documents Act (PIPEDA)
• The Personal Internet Protection and Electronic Documents Act (PIPEDA)

Question 3: In the case study, why did the ESDC’s Canada Student Loans Program (CSLP) employee make a backup copy of the program information stored in the central computer?

• He/she knew someone in the data set and wanted to see the value of their loans
• To sell the data on the dark web
• To view the data at home
• To keep the data for personal use after he/she resigned from the organization
• To protect against an accidental loss or deletion of the files during a data migration

Module 2 – Target Corp (USA) Quiz Answers

Question 1: Under The Personal Information Protection and Electronic Documents Act, PIPEDA, is credit card information considered ‘personal information’?

• False
• True

Question 2: Are bricks and mortar physical stores covered under the Personal Information Protection and Electronic Documents Act, PIPEDA?

• False
• True

Question 3: Is an online commercial business that sells jewelry online and has a maximum of 99 employees covered under the Personal Information Protection and Electronic Documents Act, PIPEDA?

• False
• True

Module 3 – Think W3 (UK) Quiz Answers

Question 1: According to General Business Law § 899-aa in the Doritex Corp. case, when should a company notify affected individuals and various government agencies of a data breach?

• In the most expedient time possible
• Within 7 days of the breach
• Within 5 business days of the breach
• Within 30 days of the breach
• At the end of the financial year

Question 2: Which of the 8 data protection principles did Think W3 UK infringe in the Case Study?

• First Principle – Personal data shall be processed fairly and lawfully and, in particular, shall not be processed unless at least one of the conditions in Schedule 2 is met and in the case of sensitive personal data, at least one of the conditions set out in Schedule 3 or either of the two Statutory Instruments below is met.
• Third principle – Personal data shall be adequate, relevant and not excessive in relation to the purpose or purposes for which they are processed.
• Fourth principle – Personal data shall be accurate and, where necessary, kept up to date.
• Seventh principle – Appropriate technical and organisational measures shall be taken against unauthorised or unlawful processing of personal data and against accidental loss or destruction of, or damage to, personal data.
• Eighth principle – Personal data shall not be transferred to a country or territory outside the European Economic Area, unless that country or territory ensures an adequate level of protection for the rights and freedoms of data
• subjects in relation to the processing of personal data.

Question 3: Which of the following statements is one of the 7 guiding principles of Privacy by Design?

• The 80/20 Rule, 20% of the team see 80% of the data
• Data access by pay grade
• Soft copy before hard copy
• Reduce waste
• Proactive not reactive

Module 4 – Home Depot (USA) Quiz Answers

Question 1: Hackers deployed custom-built malware on which Home Depot system?

• Alarm system
• Surveillance system
• Self-checkout system
• Loyalty card system
• Company Intranet

Question 2: What can companies do to prevent hacks?

• Limit the number of password attempts in a short period
• Increase the number of users on the platform
• Pay lawyers to litigate any attempts at hacking
• Host the apps and websites on international servers
• Encourage employees to save their passwords in a ‘Passwords’ folder on the company network

Question 3: From what you learnt in the ‘Tips for strong passwords’ section, which of these is considered a strong password?

• 123123
• Passw0rd1
• N3wG.UUnniiV3r5iiTeeG.yyorK
• QWERTY
• Mohammed123

Question 4: How did hackers access the Home Depot network?

• A vendor’s username and password
• A vendor’s USB stick
• An open wifi network
• A card cloning machine
• A vendor’s mobile phone

Module 5 – Privacy Tips Quiz Answers

Question 1: Which of these is on the list of ‘worst password ideas?

• Medical procedures
• Another family member’s name
• Made up words
• Phrase combinations
• Phrases in another language

Question 2: In the ’10 Privacy Tips of Companies’ list, on completion of projects, all materials relating to a project should be deleted, __________________

• including backups.
• excluding backups.

Data Privacy Fundamentals Final Exam Answers

Question 1: Why did OneStopParking put off the website update?

• Because the update broke portions of the website
• Because the website was programmed to do automatic updates
• Because no one received the notification about an update
• Because the web administrator was on maternity leave
• Because no one knew how to do the update

Question 2: OneStopParking was able to determine exactly which customers were affected by the breach:

• True
• False

Question 3: How many days after OneStopParking learned about the breach did they remedy the situation?

• 6 days
• 21 days
• 15 days
• 17 days
• 3 days

The ‘Justin’ Case Study – Multiple Choice Answers

Question 4: Based on what you have learnt in this course, which of the following options is a good Privacy by Design feature in a database system?

• the database system auto saves passwords in the browser
• the database system allows multiple attempts after an incorrect password entry
• the database system include historical personal customer data that no one uses
• the database system prompts administrators to change the password regularly
• the database system lets administrators recycle passwords

The ‘Justin’ Case Study – Multiple Choice Answers

Question 5: The company privacy policy clearly states that password problems of this nature should be reported to the IT Director immediately. Should you help Justin with his password problem?

• Yes – The request seems reasonable as long as you guess the correct password in under 5 attempts.
• No – The request seems unethical and you should advise Justin to report his password problem.
• No – The request seems unethical and you should not get involved.
• No – The request seems unethical and you should report Justin for his irresponsible password practices.
• Yes – You have the skills to help Justin with his password problem, you should offer your assistance without question.

Enroll Here: Data Science Methodology Cognitive Class Exam Quiz Answers

Introduction to Data Science Methodology

Introducing the methodology of data science involves understanding the systematic approach used to derive insights and knowledge from data. Here’s a structured overview:

1. Problem Understanding

• Define the Problem: Clearly articulate the business problem or question that data science aims to address.
• Goals: Establish measurable objectives that the analysis should achieve.
• Stakeholder Alignment: Ensure alignment between data scientists and stakeholders on goals and expectations.

2. Data Collection

• Identify Data Sources: Determine what data is available and relevant to the problem.
• Data Acquisition: Collect structured or unstructured data from various sources like databases, APIs, files, etc.
• Data Exploration: Perform initial exploration to assess data quality, understand its characteristics, and identify potential issues.

3. Data Preparation

• Data Cleaning: Handle missing data, outliers, and inconsistencies.
• Data Transformation: Format data as required (e.g., normalization, scaling).
• Feature Engineering: Create new features from existing data to improve model performance.

4. Data Modeling

• Select Modeling Techniques: Choose appropriate algorithms or methods based on the problem and data.
• Model Training: Build a predictive or descriptive model using the prepared data.
• Model Evaluation: Assess model performance using metrics relevant to the problem (e.g., accuracy, precision, recall).

5. Model Deployment

• Deploy the Model: Integrate the model into the existing business process or software infrastructure.
• Monitor Performance: Continuously monitor the model to ensure it performs as expected in real-world scenarios.
• Feedback Loop: Gather feedback from users and stakeholders to iterate and improve the model.

6. Iterate and Refine

• Iterative Process: Data science methodology often involves iterating through the above steps based on findings and feedback.
• Refinement: Improve models, data quality, or methodology based on insights gained during deployment and usage.

7. Conclusion and Reporting

• Summarize Findings: Present insights and findings to stakeholders in a clear and understandable manner.
• Document the Process: Maintain documentation of the methodology, data sources, and decisions made throughout the project.

Key Considerations

• Ethical Considerations: Address privacy, bias, and fairness issues in data usage and model development.
• Domain Knowledge: Collaboration with domain experts is crucial to ensure the relevance and applicability of the analysis.

Data science methodology provides a structured framework to navigate the complexities of working with data, ensuring that insights extracted contribute meaningfully to solving business problems or answering research questions.

Data Science Methodology Cognitive Class Certification Answers

Module 1 – From Problem to Approach Quiz Answers

Question 1: Select the correct statement.

• A methodology is an application for a computer program.
• A methodology is a set of instructions.
• A methodology is a system of methods used in a particular area of study or activity.
• All of the above statements are correct.

Question 2: Select the correct statement.

• The data science methodology described in this course is only used by certified data scientists.
• The data science methodology described in this course is outlined by John Rollins from IBM.
• The data science methodology described in this course is limited to IBM.
• None of the above statements are correct.

Question 3: Select the correct statement.

• The first stage of the data science methodology is data understanding.
• The first stage of the data science methodology is modeling.
• The first stage of the data science methodology is business understanding.
• The first stage of the data science methodology is data collection.

Module 2 – From Requirements to Collection Quiz Answers

Question 1: Select the correct statement.

• If a problem is a dish, then data is an answer.
• If a problem is a dish, then data is an ingredient.
• If a problem is a dish, then data is a list of information.
• None of the above statements are correct.

Question 2: Select the correct statement.

• A data requirement is never refined.
• A data requirement is set in stone.
• A data requirement is the initial set of ingredients.
• None of the above statements are correct.

Question 3: Select the correct statement.

• Data scientists determine how to prepare the data.
• Data scientists identify the data that is required for data modeling.
• Data scientists determine how to collect the data.
• All of the above.

Module 3 – From Understanding to Preparation Quiz Answers

Question 1: Select the correct statement about data preparation.

• Data preparation involves properly formatting the data.
• Data preparation involves correcting invalid values and addressing outliers.
• Data preparation involves removing duplicate data.
• Data preparation involves addressing missing values.
• All of the above statements are correct.

Question 2: Select the correct statement about data understanding.

• Data understanding encompasses removing redundant data.
• Data understanding encompasses all activities related to constructing the dataset.
• Data understanding encompasses sorting the data.
• All of the above statements about data understanding are correct.

Question 3: Select the correct statement about what data scientists and database administrators (DBAs) do during data preparation.

• During data preparation, data scientists and DBAs identify missing data.
• During data preparation, data scientists and DBAs determine the timing of events.
• During data preparation, data scientists and DBAs aggregate the data and merge them from different sources.
• During data preparation, data scientists and DBAs define the variables to be used in the model.
• All of the above statements are correct.

Module 4 – From Modeling to Evaluation Quiz Answers

Question 1: Select thee correct statement.

• A training set is used for data visualization.
• A training set is used for predictive modeling.
• A training set is used for statistical analysis.
• A training set is used for descriptive modeling.
• None of the above statements are correct,

Question 2: A statistician calls a false-negative, a type I error, and a false-positive, a type II error.

• True
• False

Question 3: Select the correct statement about model evaluation.

• Model evaluation can include statistical significance testing.
• Model evaluation includes ensuring that the data are properly handled and interpreted.
• Model evaluation includes ensuring the model is designed as intended.
• Model evaluation includes snsuring that the model is working as intended.
• All of the above statements are correct.

Module 5 – From Deployment to Feedback Quiz Answers

Question 1: The final stages of the data science methodology are an iterative cycle between model evaluation, deployment, and feedback.

• True
• False

Question 2: What is model evaluation used for?

• Assessing the model after getting deployed.
• Assessing the model before getting deployed.
• Determining if the model is good for other uses.
• All of the above.
• None of the above.

Question 3: Select the correct statement about the feedback stage of the data science methodology.

• Feedback is essential to the long-term viability of the model.
• Feedback is not helpful and gets in the way.
• Feedback is not required once launched.
• None of the above statements are correct.

Data Science Methodology Final Exam Answers

Question 1: Select the correct sentence about the data science methodology explained in the course.

• Data science methodology is not an iterative process – one does not go back and forth between methodological steps.
• Data science methodology is a specific strategy that guides processes and activities relating to data science only for text analytics.
• Data science methodology always starts with data collection.
• Data science methodology provides the data scientist with a framework for how to proceed to obtain answers.
• Data science methodology depends on a specific set of technologies or tools.

Question 2: Business understanding is important in the data science methodology stage. Why?

• Because it shapes the rest of the methodological steps.
• Because it clearly defines the problem and the needs from a business perspective.
• Because it ensures that the work generates the intended solution.
• Because it involves domain expertise.
• All of the above.

Question 3: A data scientist determines that building a recommender system is the solution for a particular business problem at hand. What stage of the data science methodology does this represent?

• Modeling
• Deployment
• Model evaluation
• Analytic approach
• Data understanding

Question 4: Which of the following represent the two important characteristics of the data science methodology?

• It is a highly iterative process and immediately ends when the model is deployed.
• It is not an iterative process and it never ends.
• It has no endpoint because data collection occurs before identifying the data requirements.
• It immediately ends when the model is deployed because no feedback is required.
• It is a highly iterative process and it never ends.

Question 5: What do data scientists typically use for exploratory analysis of data and to get acquainted with them?

• They use support vector machines and neural networks as feature extraction techniques.
• They begin with regression, classification, or clustering.
• They use deep learning.
• They use descriptive statistics and data visualization techniques.
• All of the above.

Question 6: Select the correct statement about data preparation.

• Data preparation cannot be accelerated through automation.
• Data preparation involves dealing with missing improperly coded data and can include using text analysis to structure unstructured or semi-structured text data.
• Data preparation is typically the least time-consuming methodological step.
• All of the above.
• None of the above.

Question 7: Which statement best describes the modeling stage of the data science methodology.

• Modeling is followed by the analytic approach stage.
• Modeling may require testing multiple algorithms and parameters.
• Modeling is always based on predictive models.
• Modeling always uses training and test sets.
• All of the above.

Question 8: Which of the following statements best describe the model evaluation stage of the data science methodology?

• Model evaluation may entail statistical significance tests, particularly when additional proof is necessary to justify some of the emerging recommendations.
• Model evaluation is important because it examines how well the model performs in the context of the business problem.
• Model evaluation entails computing graphs and/or various diagnostic measures such as a confusion matrix.
• Model evaluation is done using a test set if the model is a predictive one.
• All of the above.

Question 9: What does deploying a model into production represent?

• It represents the end of the iterative process that includes feedback, model refinement, and redeployment.
• It represents the beginning of an iterative process that includes feedback, model refinement and redeployment and requires the input of additional groups, such as marketing personnel and business owners.
• It represents the final data science product.
• None of the above.

Question 10: A data scientist, John, was asked to help reduce readmission rates at a local hospital. After some time, John provided a model that predicted which patients were more likely to be readmitted to the hospital and declared that his work was done. Which of the following best describes this scenario?

• John only provided one model as a solution and he should have provided multiple models.
• The scenario is already optimal.
• Even though John only submitted one solution, it might be a good one. However, John needed feedback on his model from the hospital to confirm that his model was able to address the problem appropriately and sufficiently.
• John’s mistake is that he lied in the analytic approach step of the data science methodology.
• John still needed to collect more data.

Question 11: A car company asked a data scientist to determine what type of customers are more likely to purchase their vehicles. However, the data comes from several sources and is in a relatively “raw format”. What kind of processing can the data scientist perform on the data to prepare it for modeling?

• Feature engineering.
• Transforming the data into more useful variables.
• Combining the data from the various sources.
• Addressing missing/invalid values.
• All of the above.

Question 12: High-performance, massively parallel systems can be used to facilitate the following methodological steps.

• Data Preparation and Modeling.
• Modeling only.
• Deployment.
• Business Understanding.
• All of the above.

Question 13: Data scientists may use either a “top-down” approach or a “bottom-up” approach to data science. These two approaches refer to:

• “Top-down” approach – the data, when sorted, is modeled from the “top” of the data towards the “bottom”. “Bottom-up” approach – the data is modeled from the “bottom” of the data to the “top”.
• “Top-down” approach – models are fit before the data is explored. “Bottom-up” approach – data is explored, and then a model is fit.
• “Top-down” approach – first defining a business problem then analyzing the data to find a solution. “Bottom-up” approach – starting with the data, and then coming up with a business problem based on the data.
• “Top-down” approach – using massively parallel, warehouses with huge data volumes as the data source. “Bottom-up” approach – using a sample of small data before using large data.
• All of the above.

Question 14: The following are all examples of rapidly evolving technologies that affect data science methodology EXCEPT for?

• Data sampling.
• Automation.
• Text analysis.
• Platform growth.
• In-database analytics.

Question 15: Data understanding involves all of the following EXCEPT for?

• Discovering initial insights about the data.
• Visualizing the data.
• Assessing data quality.
• Understanding the content of the data.
• Gathering and analyzing feedback for assessment of the model’s performance.

Question 16: For predictive models, a test set, which is similar to – but independent of – the training set, is used to determine how well the model predicts outcomes. This is an example of what step in the methodology?

• Data preparation.
• Deployment.
• Analytic approach.
• Model evaluation.
• Data requirements.

Question 17: “When ______ data is available (such as customer call center logs or physicians’ notes in unstructured or semi-structured format), _______ analytics can be useful in deriving new structured variables to enrich the set predictors and improve model accuracy.” Which of the following most appropriately fills in the blanks?

• text; text
• market; statistical
• big; digital
• highly structured; text
• text; predictive

Question 18: Typically, in a predictive model, the training set and the test set are very different and independent, such as having a different set of variables or structure.

• True
• False

Question 19: Data scientists may frequently return to a previous stage to make adjustments, as they learn more about the data and the modeling.

• True
• False

Question 20: Why should data scientists maintain continuous communication with business sponsors throughout a project?

• So that business sponsors can provide domain expertise.
• So that business sponsors can ensure the work remains on track to generate the intended solution.
• So that business sponsors can review intermediate findings.
• All of the above.
• None of the above.

Enroll Here: Prompt Engineering for Everyone Cognitive Class Exam Quiz Answers

Introduction to Prompt Engineering for Everyone

Prompt engineering is the art and science of crafting effective prompts for machine learning models, particularly those based on natural language processing (NLP). It’s a crucial skill because the quality of prompts directly influences the output and behavior of these models.

At its core, prompt engineering involves understanding both the capabilities of the model and the desired task or query you want it to perform. Here are some key aspects to consider when crafting prompts:

1. Clarity and Precision: Your prompt should clearly convey the task or question. Ambiguity can lead to unexpected or undesired outputs from the model.
2. Relevance: Ensure that the prompt is relevant to the dataset and the specific problem you’re trying to solve. Irrelevant prompts can confuse the model and reduce its effectiveness.
3. Bias and Fairness: Be mindful of biases that may be inadvertently introduced through the prompt, which could lead to biased outputs from the model.
4. Length and Format: The length and format of the prompt can influence how the model interprets it. Experimentation with different lengths and formats can help optimize performance.
5. Fine-tuning: For more advanced users, fine-tuning prompts involves tweaking them iteratively based on model outputs and performance metrics, aiming to achieve the desired results more accurately.
6. Domain Knowledge: Depending on the task, incorporating domain-specific knowledge into the prompts can improve the relevance and quality of the model’s responses.
7. Testing and Iteration: It’s often necessary to test various prompts and iterate based on the results to find the most effective one. This process can involve adjusting wording, structure, or even the underlying assumptions in the prompt.

In essence, prompt engineering democratizes access to powerful AI models by enabling users to tailor inputs according to their specific needs, regardless of their technical background. Whether you’re asking a model to summarize text, generate creative writing, or analyze data, mastering prompt engineering empowers you to leverage these advanced tools effectively.

Prompt Engineering for Everyone Cognitive Class Certification Answers

Module 1: Introduction to Prompt Engineering Quiz Answers

Question 1: Can computers inherently understand ambiguous instructions like humans do?

• Yes
• No

Question 2: Why do we historically use programming languages instead of plain English to instruct computers?

• English is easier for computers to understand.
• English is less ambiguous than programming languages.
• Programming languages allow for faster execution of tasks.
• English is more ambiguous than programming languages for providing specific instructions.

Question 3: What does the term ‘zero-shot’ prompting mean in the context of Large Language Models (LLMs)?

• The model is provided with multiple examples before making a prediction.
• The model makes a prediction without any prior examples.
• The model is trained with zero data.
• The model takes zero seconds to produce an answer.

Question 4: Naive or standard prompts typical use few-shots prompting.

• True
• False

Question 5: Is the data that AI models like LLMs are trained on always flawless?

• Yes, corportations spend billions ensuring such is the case.
• No, despite best efforts, we can’t escape flawed and biased information.

Module 2: Getting Started with Prompt Engineering Quiz Answers

Question 1: The Naive Approach to prompting the AI often results in overly generic and broad responses.

• True
• False

Question 2: Is the Persona Pattern used to make the AI adopt a specific character or identity for more customized results?

• Yes
• No

Question 3: Which of the following best describes the “Interview Pattern”?

• The AI asking the user about its training data.
• The AI interviewing the user to gather enough specific details for a customized answer.
• The user providing the AI with all details at once, without the AI asking any questions.
• The AI asking random questions regardless of the topic.

Question 4: Why would one combine the Persona Pattern with the Interview Pattern?

• To get more entertaining replies from the AI.
• To make the AI’s responses impersonal.
• To get both the viewpoint of an expert character and a detailed answer specific to us.
• There’s no practical reason to combine them.

Question 5: When requesting the AI to craft a blog post for the “Prompt Engineering for Everyone” course using the Interview Pattern, what did the AI first ask for?

• The course’s price and duration.
• Key information about the course, such as target audience and unique selling points.
• The course’s difficulty level.
• User reviews and feedback about the course.

Module 3: The Chain-of-Thought Approach Quiz Answers

Question 1: What were the two phrases mentioned that can be added to the prompt to solicit better answers by doing step-by-step reasoning?

• “Let’s solve it.” and “Break it down.”
• “Let’s think step by step.” and “Let’s work this out in a step-by-step way to be sure we have the right answer.”
• “Solve methodically.” and “Divide and conquer.”
• “Think deeply.” and “Give a comprehensive answer.”

Question 2: Using the Chain-of-Thought approach always requires retraining the AI model.

• True
• False

Question 3: Does using the Zero-Shot CoT prompting technique always produce short answers?

• Yes
• No

Question 4: In the provided example about space exploration, why was the Chain-of-Thought approach used?

• To get a quicker answer.
• To focus only on the moon landing.
• To get a more comprehensive and detailed answer by breaking down various facets of the topic.
• To get a brief summary.

Question 5: What is one downside to using the Chain-of-Thought approach as mentioned in the content?

• It’s going It requires the AI to be retrained.
• to make us an offer we can’t refuse.
• It always provides a concise answer.
• It may require knowledge of the subject or research, making it time-consuming.

Module 4: Advanced Techniques Quiz Answers

Question 1: According to researchers, the Tree-of-Thought (ToT) approach achieved a 74% success rate in the Game of 24, while Chain-of-Thought only achieved 4%.

• True
• False

Question 2: What does the Tree-of-Thought (ToT) prompting encourage the AI to do?

• Follow a linear sequence of thoughts.
• Build upon intermediate thoughts and explore branches.
• Think really hard.
• Follow a fixed set of instructions.

Question 3: Which of the following can be considered a benefit of the ToT approach?

• It always gives a concise answer.
• It provides multiple viewpoints akin to brainstorming.
• It focuses on a singular expert perspective.
• It reduces the depth of the answer to make it more generic.

Question 4: What purpose does controlling verbosity serve in the model’s response?

• To increase the length of every answer.
• To modify the depth of detail in the response.
• To improve the accuracy of the answer.
• To limit the model to short responses only.

Question 5: In the Nova System, who is responsible for ensuring the conversation remains on topic?

• The Critical Evaluation Expert (CAE).
• The Critical Execution Expert (CAE)
• The User.
• The Discussion Continuity Expert (DCE).

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

Introduction to Data Analysis with Python

Data analysis involves inspecting, cleaning, transforming, and modeling data to uncover useful information, draw conclusions, and support decision-making.

Why Python for Data Analysis?

Python is popular for data analysis due to its versatility, extensive libraries, and readability.

Essential Libraries for Data Analysis in Python:

1. NumPy: Fundamental package for numerical computing with arrays.
2. Pandas: Powerful library for data manipulation and analysis, offering data structures like DataFrames.
3. Matplotlib: Comprehensive library for creating static, animated, and interactive visualizations.
4. Seaborn: Built on Matplotlib, Seaborn provides a high-level interface for drawing attractive statistical graphics.

Steps in Data Analysis with Python:

1. Importing Data: Load data from various sources (CSV, Excel, databases, web APIs).
2. Data Exploration: Understand the structure, variables, and basic statistics of the dataset.
3. Data Cleaning: Handle missing values, outliers, and inconsistencies.
4. Data Visualization: Create visual representations to explore patterns and relationships.
5. Data Transformation: Prepare data for modeling by encoding categorical variables, scaling features, etc.
6. Statistical Modeling: Apply statistical methods or machine learning algorithms for analysis.
7. Evaluation and Interpretation: Assess model performance, interpret results, and draw conclusions.

Python’s ecosystem is vast, so continuous learning and practice will enhance your proficiency in data analysis.

Data Analysis with Python Cognitive Class Certification Answers

Module 1 – Introduction Quiz Answers

Question 1: What does CSV stand for?

• Comma-separated values
• Car sold values
• Car state values
• None of the above

Question 2: In the data set, which of the following represents an attribute or feature?

• Row
• Column
• Each element in the dataset

Question 3: What is the name of what we want to predict?

• Target
• Feature
• Dataframe

Question 4: What is the command to display the first five rows of a dataframe df?

• df.head()
• df.tail()

Question 5: What command do you use to get the data type of each row of the dataframe df?

• df.dtypes
• df.head()
• df.tail()

Question 6: How do you get a statistical summary of a dataframe df?

• df.describe()
• df.head()
• df.tail()

Question 7: If you use the method describe() without changing any of the arguments, you will get a statistical summary of all the columns of type “object”.

• False
• True

Module 2 – Data Wrangling Quiz Answers

Question 1: Consider the dataframe df. What is the result of the following operation: df[‘symbolling’] = df[‘symbolling’] + 1?

• Every element in the column “symbolling” will increase by one.
• Every element in the row “symbolling” will increase by one.
• Every element in the dataframe will increase by one.

Question 2: Consider the dataframe df. What does the command df.rename(columns={‘a’:’b’}) change about the dataframe df?

• Renames column “a” of the dataframe to “b”.
• Renames row “a” to “b”.
• Nothing. You must set the parameter “inplace = True”.

Question 3: Consider the dataframe “df”. What is the result of the following operation df[‘price’] = df[‘price’].astype(int)?

• Convert or cast the row ‘price’ to an integer value.
• Convert or cast the column ‘price’ to an integer value.
• Convert or cast the entire dataframe to an integer value.

Question 4: Consider the column of the dataframe df[‘a’]. The column has been standardized. What is the standard deviation of the values as a result of applying the following operation: df[‘a’].std()?

• 1
• 0
• 3

Question 5: Consider the column of the dataframe, df[‘Fuel’], with two values: ‘gas’ and’ diesel’. What will be the name of the new columns pd.get_dumies(df[‘Fuel’]) ?

• 1 and 0
• Just ‘diesel’
• Just ‘gas’
• ‘gas’ and ‘diesel’

Question 6: What are the values of the new columns from part?

• 1 and 0
• Just ‘diesel’
• Just ‘gas’
• ‘gas’ and ‘diesel’

Module 3 – Exploratory Data Analysis Quiz Answers

Question 1: Consider the dataframe “df”. What method provides the summary statistics?

• df.describe()
• df.head()
• df.tail()

Question 2: Consider the following dataframe:

df_test = df[‘body-style’, ‘price’]

The following operation is applied:

df_grp = df_test.groupby([‘body-style’], as_index=False).mean()

What are resulting values of df_grp[‘price’]?

• The average price for each body style.
• The average price.
• The average body style.

Question 3: Correlation implies causation:

• False
• True

Question 4: What is the minimum possible value of Pearson’s Correlation?

• 1
• -100
• -1

Question 5: What is the Pearson correlation between variables X and Y if X=Y:

• -1
• 1
• 0

Module 4 – Model Development Quiz Answers

Question 1: Let X be a dataframe with 100 rows and 5 columns. Let y be the target with 100 samples. Assuming all the relevant libraries and data have been imported, the following line of code has been executed:

LR = LinearRegression()

LR.fit(X, y)

yhat = LR.predict(X)

How many samples does yhat contain?

• 5
• 500
• 100
• 0

Question 2: What value of R^2 (coefficient of determination) indicates your model performs best?

• -100
• -1
• 0
• 1

Question 3: Which statement is true about polynomial linear regression?

• Polynomial linear regression is not linear in any way.
• Although the predictor variables of polynomial linear regression are not linear, the relationship between the parameters or coefficients is linear.
• Polynomial linear regression uses wavelets.

Question 4: The larger the mean squared error, the better your model performs:

• False
• True

Question 5: Assume all the libraries are imported. y is the target and X is the features or dependent variables. Consider the following lines of code:

Input=[(‘scale’,StandardScaler()),(‘model’,LinearRegression())]

pipe=Pipeline(Input)

pipe.fit(X,y)

ypipe=pipe.predict(X)

What is the result of ypipe?

• Polynomial transform, standardize the data, then perform a prediction using a linear regression model.
• Standardize the data, then perform prediction using a linear regression model.
• Polynomial transform, then standardize the data.

Module 5 – Model Evaluation Quiz Answers

Question 1: In the following plot, the vertical axis shows the mean square error and the horizontal axis represents the order of the polynomial. The red line represents the training error the blue line is the test error. What is the best order of the polynomial given the possible choices in the horizontal axis?

• 2
• 8
• 16

Question 2: What is the correct use of the “train_test_split” function such that 40% of the data samples will be utilized for testing; the parameter “random_state” is set to zero; and the input variables for the features and targets are_data, y_data respectively?

• train_test_split(x_data, y_data, test_size=0, random_state=0.4)
• train_test_split(x_data, y_data, test_size=0.4, random_state=0)
• train_test_split(x_data, y_data)

Question 3: What is the output of cross_val_score(lre, x_data, y_data, cv=2)?

• The predicted values of the test data using cross-validation.
• The average R^2 on the test data for each of the two folds.
• This function finds the free parameter alpha.

Question 4: What is the code to create a ridge regression object “RR” with an alpha term equal 10?

• RR=LinearRegression(alpha=10)
• RR=Ridge(alpha=10)
• RR=Ridge(alpha=1)

Question 5: What dictionary value would we use to perform a grid search for the following values of alpha: 1,10, 100? No other parameter values should be tested.

• alpha=[1,10,100]
• [{‘alpha’: [1,10,100]}]
• [{‘alpha’: [0.001,0.1,1, 10, 100, 1000,10000,100000,100000],’normalize’:[True,False]} ]

Data Analysis with Python Final Exam Answers

Question 1: What does the following command do?

df.dropna(subset=[“price”], axis=0)

• Drop the “not a number” values from the column “price”.
• Drop the row “price”.
• Rename the dataframe “price”.

Question 2: How would you provide many of the summary statistics for all the columns in the dataframe “df”?

• df.describe(include = “all”)
• df.head()
• type(df)
• df.shape

Question 3: How would you find the shape of the dataframe df?

• df.describe()
• df.head()
• type(df)
• df.shape

Question 4: What task does the following command, df.to_csv(“A.csv”), perform:

• Change the name of the column to “A.csv”.
• Load the data from a csv file called “A” into a dataframe.
• Save the dataframe df to a csv file called “A.csv”.

Question 5: What task does the following line of code perform?

result = np.linspace(min(df[“city-mpg”]), max(df[“city-mpg”]), 5)

• Builds a bin array ranging from the smallest value to the largest value of “city-mpg” in order to build 4 bins of equal length.
• Builds a bin array ranging from the smallest value to the largest value of “city-mpg” in order to build 5 bins of equal length.
• Determines which bin each value of “city-mpg” belongs to.

Question 6: What task does the following line of code perform:

df[‘peak-rpm’].replace(np.nan, 5,inplace=True)

• Replace the “not a number” values with 5 in the column ‘peak-rpm’.
• Rename the column ‘peak-rpm’ to 5.
• Add 5 to the dataframe.

Question 7: How do you “one-hot encode” the column ‘fuel-type’ in the dataframe df?

• pd.get_dummies(df[“fuel-type”])
• df.mean([“fuel-type”])
• df[df[“fuel-type”])==1 ]=1

Question 8: What does the vertical axis on a scatterplot represent?

• Independent variable
• Dependent variable

Question 9: What does the horizontal axis on a scatterplot represent?

• Independent variable
• Dependent variable

Question 10: If we have 10 columns and 100 samples, how large is the output of df.corr()?

• 10 x 100
• 10 x 10
• 100×100
• 100×100

Question 11: What is the largest possible element resulting in the following operation “df.corr()”?

• 100
• 1000
• 1

Question 12: If the Pearson Correlation of two variables is zero:

• The two variable have zero mean.
• The two variables are not correlated.

Question 13: If the p-value of the Pearson Correlation is 1:

• The variables are correlated.
• The variables are not correlated.
• None of the above.

Question 14: What does the following line of code do: lm = LinearRegression()?

• Fit a regression object “lm”.
• Create a linear regression object.
• Predict a value.

Question 15: If the predicted function is:

Yhat = a + b1 X1 + b2 X2 + b3 X3 + b4 X4

The method is:

• Polynomial Regression
• Multiple Linear Regression

Question 16: What steps do the following lines of code perform:

Input=[(‘scale’,StandardScaler()),(‘model’,LinearRegression())]

pipe=Pipeline(Input)

pipe.fit(Z,y)

ypipe=pipe.predict(Z)

• Standardize the data, then perform a polynomial transform on the features Z.
• Find the correlation between Z and y.
• Standardize the data, then perform a prediction using a linear regression model using the features Z and targets y.

Question 17: What is the maximum value of R^2 that can be obtained?

• 10
• 1
• 0

Question 18: We create a polynomial feature PolynomialFeatures(degree=2). What is the order of the polynomial?

• 0
• 1
• 2

Question 19: You have a linear model. The average R^2 value on your training data is 0.5. You perform a 100th order polynomial transform on your data, then use these values to train another model. Your average R^2 is 0.99. Which comment is correct?

• 100th order polynomial will work better on unseen data.
• You should always use the simplest model.
• The results on your training data is not the best indicator of how your model performs. You should use your test data to get a beter idea.

Question 20: You train a ridge regression model. You get a R^2 of 1 on your validation data and you get a R^2 of 0.5 on your training data. What should you do?

• Nothing. Your model performs flawlessly on your validation data.
• Your model is under fitting perform a polynomial transform.
• Your model is overfitting, increase the parameter alpha.

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

Introduction to Python for Data Science

Python is an incredibly popular programming language for data science due to its simplicity and versatility. Here’s a brief introduction to Python for data science:

Why Python for Data Science?

1. Ease of Learning: Python has a straightforward and readable syntax, making it accessible for beginners.
2. Rich Ecosystem: It has extensive libraries and frameworks specifically for data science, such as NumPy, Pandas, Matplotlib, and Scikit-learn.
3. Versatility: Python is not just limited to data science; it’s used in web development, automation, scripting, etc., making it a versatile choice for various tasks.

Key Libraries for Data Science in Python

1. NumPy: Essential for numerical computing with support for large, multi-dimensional arrays and matrices.
2. Pandas: Offers data structures and tools for data manipulation and analysis, especially useful for working with structured data.
3. Matplotlib: Comprehensive library for creating static, animated, and interactive visualizations in Python.
4. Scikit-learn: Simple and efficient tools for data mining and data analysis, particularly for building machine learning models.

Basic Python Concepts

1. Variables and Data Types: Variables are used to store data values. Python supports various data types like integers, floats, strings, lists, tuples, dictionaries, etc.
2. Control Structures: Includes if statements, loops (for and while loops), and functions for organizing and controlling the flow of execution.
3. Functions: Blocks of reusable code that perform a specific task. They help in organizing code and promoting code reusability.
4. Packages and Modules: Python code can be organized into packages (directories of Python scripts) and modules (Python files containing functions, classes, etc.).

Getting Started

To get started with Python for data science:

• Install Python from python.org
• Use package managers like pip or conda to install libraries (pip install pandas, conda install numpy, etc.)
• Start coding with Jupyter notebooks or your preferred IDE (PyCharm, VS Code, etc.)

Conclusion

Python’s simplicity, powerful libraries, and active community make it an excellent choice for anyone getting into data science. Whether you’re analyzing data, building machine learning models, or creating visualizations, Python has the tools and support you need to succeed.

Python For Data Science Cognitive Class Certification Answers

Module 1 – Python Basics Quiz Answers

Question 1: What is the result of the following operation in Python:

3 + 2 * 2

• 10
• 7
• 9
• 12

Question 2: In Python, if you executed name = ‘Lizz’, what would be the output of print(name[0:2])?

• Lizz
• L
• Liz
• Li

Question 3: In Python, if you executed var = ‘01234567’, what would be the result of print(var[::2])?

• 0246
• 1357
• 1234567
• 8903

Question 4: In Python, what is the result of the following operation ‘1’+’2′?

• ‘2’
• ‘3’
• ’12’
• 3

Question 5: Given myvar = ‘hello’, how would you convert myvar into uppercase?

• len(myvar)
• myvar.find(‘hello’)
• myvar.upper()
• myvar.sum()

Module 2 – Python Data Structures Quiz Answers

Question 1: What is the syntax used to obtain the first element of the tuple:

A = (‘a’,’b’,’c’)

• A[1]
• A[0]
• A[:]

Question 2: After applying the following method, L.append([‘a’,’b’]), the following list will only be one element longer.

• True
• False

Question 3: How many duplicate elements can you have in a set?

• 1
• 0. You can only have one unique element in a set.
• 100
• Depends on the number of elements in your set.

Question 4: Consider the following Python dictionary:

Dict={“A”:1,”B”:”2″,”C”:[3,3,3],”D”:(4,4,4),’E’:5,’F’:6}

What is the result of the following operation: Dict[“D”]?

• 4
• 3
• [3,3,3]
• (4, 4, 4)
• Error

Question 5: What is an important difference between lists and tuples?

• Lists can’t contain a string.
• Tuples can only have integers.
• Lists and tuples are the same.
• Lists are mutable, tuples are not.
• There are no zeros in lists.

Module 3 – Python Programming Fundamentals Quiz Answers

Question 1: What is the output of the following lines of code:

x=1

if(x!=1):

            print(‘Hello’)

else:

            print(‘Hi’)

print(‘Mike’)

• Hi Mike
• Mike
• Hello Mike
• The Mike

Question 2: What is the output of the following few lines of code?

A = [‘1′,’2′,’3’]

for a in A:

            print(2*a)

• 2 4 6
• ‘2’ ‘4’ ‘6’
• ’11’ ’22’ ’33’
• A B C

Question 3: Consider the function Delta, when will the function return a value of 1

            def Delta(x):

                        if x==0:

                                  y=1;

                        else:

                                    y=0;

                         return(y)

• When the input is anything but 0.
• When the input is 1.
• Never.
• When the input is 0.

Question 4: What is the correct way to sort the list ‘B’ using a method? The result should not return a new list, just change the list ‘B’.

• B.sort()
• sort(B)
• sorted(B)
• B.sorted()

Question 5: What are the keys of the following dictionary: {‘a’:1,’b’:2}?

• 1,2
• ;,:
• a,b

Module 4 – Working with Data in Python Quiz Answers

Question 1: What do the following lines of code do?

with open(“Example1.txt”,”r”) as file1:

            FileContent=file1.readlines()

            print(FileContent)

• Read the file “Example1.txt”.
• Write to the file “Example1.txt”.
• Append the file “Example1.txt”.

Question 2: What do the following lines of code do?

with open(“Example2.txt”,”w”) as writefile:

            writefile.write(“This is line A\n”)

            writefile.write(“This is line B\n”)

• Read the file “Example2.txt”.
• Write to the file “Example2.txt”.
• Append the file “Example2.txt”.

Question 3: What do the following lines of code do?

with open(“Example3.txt”,”a”) as file1:

            file1.write(“This is line C\n”)

• Read the file “Example3.txt”.
• Write to the file “Example3.txt”.
• Append the file “Example3.txt”.

Question 4: What is the result of applying the following method df.head() to the dataframe “df”?

• Prints the first row of the dataframe.
• Prints the first column of the dataframe.
• Prints the first 5 rows of the dataframe.
• Prints out the entire dataframe.

Module 5 – Working with Numpy Arrays & Simple APIs Quiz Answers

Question 1: What is the result of the following lines of code:

a=np.array([0,1,0,1,0])

b=np.array([1,0,1,0,1])

a*b

• 0
• array([1, 1, 1, 1, 1])
• array([0, 0, 0, 0, 0])

Question 2: What is the result of the following lines of code:

a=np.array([0,1])

b=np.array([1,0])

np.dot(a,b)

• 1
• array([1,1])
• 0
• array([0,0])

Question 3: What is the result of the following lines of code:

a=np.array([1,1,1,1,1])

a+10

• array([10,10,10,10,10])
• array([11, 11, 11, 11, 11])
• array([1,1,1,1,1])

Question 4: What is the correct code to perform matrix multiplication on the matrix A and B?

• np.dot(A,B)
• A*B
• AxB

Python For Data Science Final Exam Answers

1) What is the result of the following operation 3+2*2?

• 3
• 12
• 9
• 7

2) What is the type of the following variable: True?

• int
• bool
• str
• list

3) What is the result of the following operation int(3.2)?

• 3.2
• 3
• 4
• ‘3.2’

4) Consider the string A=’1234567′, what is the result of the following operation: A[1::2]

• ‘1234567’
• ‘246’
• ‘1357’
• error

5) Consider the string Name=”Michael Jackson” , what is the result of the following operation Name.find(‘el’)

• 5
• 4
• 5,6
• -1

6) The variables A=’1′ and B=’2′ ,what is the result of the operation A+B?

you cant add two strings

• 3
• ‘3’
• ’12’

7) Consider the variable F=”You are wrong”, Convert the values in the variable F to uppercase?

• F.up()
• F.upper
• F.upper()

8) Consider the tuple tuple1=(“A”,”B”,”C” ), what is the result of the following operation tuple1[-1]?

• “A”
• “B”
• “C”

9)Consider the tuple A=((11,12),[21,22]), that contains a tuple and list. What is the result of the following operation A[1]:

• ((11,12),[21,22])
• (11,12)
• (21,22)
• [21,22]

10) Consider the tuple A=((11,12),[21,22]), that contains a tuple and list. What is the result of the following operation A[0][1]:

• 12
• 11
• 22
• 21

11) What is the result of the following operation ‘1,2,3,4’.split(‘,’)

• ‘1’,’2′,’3′,’4′
• (‘1′,’2′,’3′,’4’)
• [‘1′,’2′,’3′,’4’]
• ‘1234’

12) Concatenate the following lists A=[1,’a’] and B=[2,1,’d’]:

• A+B
• A-B
• A*B
• A/B

13) How do you cast the list ‘A’ to the set ‘a’?

• a.set()
• a=A.append()
• a=A.dict()
• a=set(A)

14) Consider the Set: V={‘A’,’B’}, what is the result of V.add(‘C’)?

• {‘A’,’B’}
• {‘A’,’B’,’C’}
• {‘AC’,’BC’}
• error

15) Consider the Set: V={‘A’,’B’,’C’ }, what is the result of V.add(‘C’)?

• {‘A’,’B’}
• {‘A’,’B’,’C’}
• {‘A’,’B’,’C’,’C’}
• error

16) What is the output of the following lines of code:

x=”Go”

if(x!=”Go”):

            print(‘Stop’)

else:

            print(‘Go ‘)

print(‘Mike’)

• Go Mike
• Mike
• Stop Mike
• The Mike

17) What is the output of the following lines of code:

x=”Go”

if(x==”Go”):

            print(‘Go ‘)

else:

            print(‘Stop’)

print(‘Mike’)

• Go Mike
• Mike
• Stop Mike
• The Mike

18) how many iterations are performed in the following loop?

for n in range(3):

            print(n)

• 1
• 2
• 3
• 4

19) What does the following loop print?

for n in range(3):

            print(n+1)

• 0 1 2
• 1 2 3
• 3 2 1
• 2 1 0

20) What is the output of the following few lines of code ?

A=[‘1′,’2′,’3’]

for a in A:

            print(2*a)

• 2 4 6
• ‘2’ ‘4’ ‘6’
• ’11’ ’22’ ’33’
• A B C

21) Consider the function add, what is the result of calling the following Add(‘1′,’1’) (look closely at the return statement )

def Add(x,y):

            z=y+x

            return(y)

• error
• ‘2’
• ’11’
• ‘1’

22) Consider the class Points, what are the data attributes:

class Points(object):

            def __init__(self,x,y):

                        self.x=x

                        self.y=y

def print_point(self):

            print(‘x=’,self.x,’y=’,self.y)

• __init__
• self.x self.y
• print_point

23) What is the result of running the following lines of code ?

class Points(object):

            def __init__(self,x,y):

                        self.x=x

                        self.y=y

def print_point(self):

            print(‘x=’,self.x,’ y=’,self.y)

p1=Points(1,2)

p1.print_point()

• x=1
• y=2
• x=1 y=2

24) What is the result of running the following lines of code ?

class Points(object):

            def __init__(self,x,y):

                        self.x=x

                        self.y=y

def print_point(self):

            print(‘x=’,self.x,’ y=’,self.y)

p2=Points(1,2)

p2.x=2

p2.print_point()

• x=1
• y=2
• x=1 y=2
• x=2 y=2

25) Consider the following line of code: with open(example1,”r”) as file1:

What mode is the file object in?

• read
• write
• append