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Enroll Here: Node-RED: basics to bots Cognitive Class Exam Quiz Answers

Introduction to Node-RED: basics to bots

Node-RED is a powerful tool for wiring together hardware devices, APIs, and online services in a visual manner. It’s particularly popular for IoT (Internet of Things) applications but can be used for a wide range of automation tasks. Here’s an introduction to Node-RED covering basics and how to create bots:

What is Node-RED?

Node-RED is an open-source flow-based development tool developed by IBM Emerging Technology. It provides a browser-based editor that makes it easy to wire together flows using a drag-and-drop interface. Each flow consists of nodes, which represent different devices, APIs, or services, connected together to perform a task.

Basics of Node-RED:

1. Flow: A flow in Node-RED is a sequence of nodes wired together. Each node performs a specific task or function.
2. Node: Nodes are the basic building blocks of a flow. They can represent input devices (sensors), output devices (actuators), functions (data processing), or connectors to APIs and services.
3. Dashboard: Node-RED provides a dashboard UI where you can create a visual interface to control and monitor your flows.
4. Palette: The palette is where you can find all available nodes. It includes nodes for HTTP requests, file operations, function nodes (for JavaScript code), and nodes for integrating with various services like Twitter, Slack, databases, etc.
5. Inject Node: This is a special node in Node-RED used to trigger the flow manually or at specified intervals for testing or automation purposes.
6. Debug Node: Helps in debugging by displaying messages from the flow.

Creating Bots with Node-RED:

Creating bots with Node-RED involves integrating with messaging platforms like Slack, Telegram, or Discord. Here’s a basic outline:

1. Install Nodes for Messaging Platforms: Node-RED has nodes available for popular messaging platforms. You can find these nodes in the Node-RED library or install them via the palette manager in Node-RED.
2. Set Up Authentication: Configure authentication tokens or API keys provided by the messaging platform to connect Node-RED to the platform’s API.
3. Receive Messages: Use input nodes specific to the messaging platform (e.g., Slack input node) to receive messages sent to the bot.
4. Process Messages: Use function nodes or existing nodes to process incoming messages. This can involve parsing messages, executing commands, or triggering specific actions based on message content.
5. Send Messages: Use output nodes specific to the messaging platform (e.g., Slack output node) to send messages back to users.
6. Deploy and Test: Deploy your flow in Node-RED and test the bot by interacting with it through the messaging platform.

Conclusion:

Node-RED simplifies the development of automation tasks, including bot creation, through its visual interface and extensive library of nodes. Whether you’re integrating IoT devices or building bots for messaging platforms, Node-RED’s flexibility and ease of use make it a valuable tool in your toolkit.

Node-RED: basics to bots Cognitive Class Certification Answers

Lab 1 – Create Your First Node-RED Flow Quiz Answers

Question 1: You can run Node-RED on the IBM Cloud without installing it locally on your computer.

• True
• False

Question 2: You can connect services like Watson Assistant to a Node-RED instance running on IBM Cloud.

• True
• False

Question 3: The three main types of nodes are Input, Output, and Objects.

• True
• False

Question 4: You use the Debug tab in the flow editor to read the documentation for a node.

• True
• False

Question 5: An inject node is a type of input node.

• True
• False

Lab 2 – Build a Web Page and Create a REST API in Node-RED Quiz Answers

Question 1: The HTTP In node can be used to create URLs for web pages and APIs.

• True
• False

Question 2: A function node can be used to process the output of a node before feeding it in input to another node.

• True
• False

Question 3: Node-RED offers a Template node.

• True
• False

Question 4: Template nodes can contain JavaScript code.

• True
• False

Question 5: language identify is a node made available by the Watson Language Translator service.

• True
• False

Lab 3 – Translate text, analyze tone, add audio, and send tweets from your Node-RED Application Quiz Answers

Question 1: You can add more functionalities by leveraging nodes from both the Node-RED project and the wider community.

• True
• False

Question 2: Microphone and speaker nodes are available through community libraries.

• True
• False

Question 3: The Node-RED community has created nodes for every possible need.

• True
• False

Question 4: For the language translator node to become available, you can use API keys to access the service or connect (bind) the service.

• True
• False

Question 5: We can use a function node to process the output of a node, preparing it for the expected input of a different node.

• True
• False

Lab 4 – Create Cognitive Webpages and Messenger Bot Quiz Answers

Question 1: Community nodes are the only way to integrate with third-party web services.

• True
• False

Question 2: You can import a JSON flow from your clipboard into Node-RED by clicking Import > Clipboard.

• True
• False

Question 3: Integrating Facebook Messenger in your Node-RED application requires that you first obtain a Page Access Token and Verify Token from Facebook.com.

• True
• False

Question 4: The facebook messenger node can only read messages from Facebook Messenger, not write them.

• True
• False

Question 5: We can transcribe and translate captions for a video by leveraging Watson Speech to Text and Watson Language Translator.

• True
• False

Lab 5 – Build HTML Web pages that use Git Respositories that can Reuse brower-side JavaScript Quiz Answers

Question 1: You can pass a video file straight into the Speech to Text Service

• True
• False

Question 2: The HTTP input node is limited to GET requests.

• True
• False

Question 3: There are three HTTP core nodes, http (input), http responde (output), and http request (function).

• True
• False

Question 4: The http input node is used to send HTTP requests.

• True
• False

Question 5: The http request node is used to send HTTP requests.

• True
• False

Node-RED: Basics to Bots Final Exam Answers

Question 1: What do you use the inject node for?

• Inject a file
• Initiate a flow
• Define an HTTP route
• As a camera

Question 2: The Watson Language Identify node expects its input text to be provided in what object?

• msg.lang
• msg.languages
• msg.payload
• msg.input

Question 3: For Node-RED on IBM Cloud, how do you install additional nodes?

• Add new dependencies in package.json
• Add new dependencies in manifest.yml
• Add new dependencies in settings.js
• Add new dependencies in red.js

Question 4: What can you use function nodes for?

• Define HTML templates
• Create virtual wires between flows
• Create JavaScript code blocks
• Define new nodes

Question 5: The Watson Speech to Text node outputs to which object?

• msg.transcription
• msg.payload
• msg.translation
• msg.result

Question 6: What are the three main categories of nodes?

• input, inject, function
• storage, input, output
• input, output, function
• analysis, advanced, output

Question 7: In this course, the microphone node from Lab 3 is available as a core Node-RED node.

• True
• False

Question 8: What does the Watson Tone Analyzer service do?

• Analyzes emotions, such as happy, sad, confident, anger, in text
• Predicts personality characteristics, such as needs and values, in text
• Looks for trends and patterns in text
• Analyzes text to extract metadata from content such as concepts, entities, keywords, categories

Question 9: The http request node is used to send HTTP requests.

• True
• False

Question 10: Node-RED is based on which programming language?

• Python
• Ruby
• Java
• Node.js

Enroll Here: Build Your Own Chatbot Cognitive Class Exam Quiz Answers

Introduction to Build Your Own Chatbot

Building your own chatbot can be an exciting journey into the world of artificial intelligence and natural language processing. Whether you’re creating a chatbot for personal use, business purposes, or just to experiment with AI technology, there are several key steps and considerations to keep in mind. Here’s a structured introduction to help you get started:

1. Define Your Purpose and Audience

• Purpose: Decide why you want to build a chatbot. Is it for customer service, entertainment, education, or something else?
• Audience: Identify who will be interacting with your chatbot. Understanding your audience helps tailor the chatbot’s personality and functionalities.

2. Choose a Platform or Framework

• Platform Options: Decide whether you’ll build your chatbot from scratch using a programming language like Python or use a chatbot development platform like Dialogflow, Microsoft Bot Framework, or Rasa.
• Considerations: Factors like ease of use, integration capabilities, and scalability should influence your choice.

3. Design the Conversation Flow

• Storyboarding: Plan out how you want the conversation to flow. Consider different user inputs and the appropriate responses.
• User Experience: Design an intuitive and user-friendly interface for interacting with the chatbot.

4. Implement Your Chatbot

• Coding: If you’re developing from scratch, start coding based on the platform or framework you’ve chosen.
• Integrate APIs: Depending on the functionalities you want your chatbot to have (e.g., accessing weather information, making reservations), integrate relevant APIs.

5. Train and Test Your Chatbot

• Natural Language Understanding (NLU): Train your chatbot to understand various user inputs using NLU techniques.
• Testing: Test your chatbot extensively to identify and fix any bugs or issues. Solicit feedback from users to improve its performance.

6. Deploy Your Chatbot

• Choose Deployment Environment: Decide where your chatbot will be deployed—on a website, messaging platform (like Facebook Messenger), or as a standalone application.
• Monitor Performance: Monitor how your chatbot performs in real-world scenarios and make adjustments as needed.

7. Iterate and Improve

• Feedback Loop: Continuously gather user feedback and analytics to iterate on your chatbot’s design and functionality.
• Updates: Regularly update your chatbot to incorporate new features, improve responses, and adapt to changing user needs.

8. Consider Ethical Implications

• Privacy and Security: Ensure your chatbot handles user data responsibly and securely.
• Bias and Fairness: Mitigate biases in your chatbot’s responses and ensure fairness in its interactions.

By following these steps, you can embark on a successful journey to create your own chatbot, tailored to your specific goals and audience. Whether you’re a beginner or an experienced developer, building a chatbot provides a hands-on opportunity to explore the capabilities and intricacies of AI-driven conversation agents.

Build Your Own Chatbot Cognitive Class Certification Answers

Module 1 – Introduction to Chatbots Quiz Answers

Question 1: A chatbot will typically interact with the user via text or audio

• True
• False

Question 2: A chatbot is a bot that interacts with the user through a chat/conversational interface

• True
• False

Question 3: Chatbots are also known as:

• Chatterbots
• Artificial Conversational Entities (ACE)
• Whatsapp
• Talkbots
• Virtual Assistants

Question 4: What factors directly contributed to the emergence of chatbots?

• AI / Cognitive Computing
• Blockchain
• Messaging platforms like Facebook Messenger
• Ruby on Rails

Question 5: Chatbots are only beneficial to large companies

• True
• False

Module 2 – Working with Intents Quiz Answers

Question 1: Watson Assistant can ONLY be used to create chatbots in English

• True
• False

Question 2: Select all the statements that are true

• An intent is a purpose or goal expressed by the user’s input
• Intents can have spaces in their names
• Intents start with an # symbol
• We train Watson by providing examples for our intents
• We should provide Watson with at least 5 examples per intent

Question 3: The three main components of a Dialog Skill are Intents, Entities, and Dialog.

• True
• False

Question 4: If Watson detects the wrong intent, we can train Watson by selecting a different intent from the Try it out panel.

• True
• False

Question 5: The Content Catalog offers collections of pre-made intents for various industries.

• True
• False

Module 3 – Working with Entities Quiz Answers

Question 1: Entities allow us to capture specific information in the user input

• True
• False

Question 2: Entities start with an # symbol

• True
• False

Question 3: Entity values can have synonyms and patterns

• True
• False

Question 4: Two user questions can have the same intent but different entities

• True
• False

Question 5: In our flower shop chatbot, “daddy” is defined as a synonym for @relationship:father. If a user were to ask “flowers for daddy”, what’s the entity value detected by Watson?

• @relationship:daddy
• @relationship:father
• @daddy
• @relationship
• @father

Module 4 – Defining the Dialog Quiz Answers

Question 1: Multiple conditional responses allow us to attach conditions to responses within a node.

• True
• False

Question 2: The order of nodes in the dialog can affect how the chatbot works.

• True
• False

Question 3: Node A has @occasion as its condition. Node B, placed just below node A, has @occasion:Graduation as its condition. Which one of the following statements is true?

• The order of node A and node B doesn’t matter.
• Both node A and node B will never be executed.
• Node A (with @occasion as a condition) is overshadowed and will not be executed (unless we explicitly jump to it).
• Node B (with @occasion:Graduation as a condition) is overshadowed and will not be executed (unless we explictily jump to it).

Question 4: In general, child nodes are considered if the parent node condition is met

• True
• False

Question 5: Select all the statements that are true

• When designing a chatbot, we should consider tone and personality
• We should avoid “yes” and “no” answers when possible
• Very long responses are good
• The tone and personality of our chatbot can affect how well it is perceived by the user
• Chatbot prompts should be as generic as possible (e.g., Hello. Ask me anything.)

Module 5 – Deploying Your Chatbot Quiz Answers

Question 1: The Watson Assistant plugin for WordPress allows us to deploy a chatbot by specifying the credentials of the corresponding Assistant, without the need to develop a separate application that connects Watson Assistant to our site.

• True
• False

Question 2: A preview link integration allows us to share our chatbot with friends and colleagues.

• True
• False

Question 3: One or more skills can be linked to an Assistant. In other words, an Assistant can “contain” one or more skills.

• True
• False

Question 4: The WordPress plugin for Watson Assistant allow us to customize the look and feel of the chat box that appears on the site.

• True
• False

Question 5: There is no way to limit the chatbot usage in the Watson Assistant WordPress plugin (to prevent abusive users).

• True
• False

Module 6 – Advanced Concepts – Part 1 Quiz Answers

Question 1: Once set, context variables can be accessed for the duration of the conversation with a given user.

• True
• False

Question 2: Slots allow us to collect information from the user and store it in context variables.

• True
• False

Question 3: Slots with no question defined are optional and will only set the context variable if the condition (e.g., @location) is detected.

• True
• False

Question 4: A node can only have one slot and therefore cannot assign more than one context variable.

• True
• False

Question 5: A required slot will only ask its question to the user once, even if the user replies with irrelevant information.

• True
• False

Module 7 – Advanced Concepts – Part 2 Quiz Answers

Question 1: The “Found” section of a slot allow us to specify what to say to the user (e.g., thanking them) when they provide a valid reply to the slot.

• True
• False

Question 2: The “Not Found” section of a slot allow us to specify what to say to the user when they provide an invalid reply to the slot (e.g., a reply that doesn’t meet the slot condition).

• True
• False

Question 3: Digressions allows us to decide what the chatbot should do when the user asks a different question instead of replying to the slot question.

• True
• False

Question 4: Handlers are evaluated after the “Not found” responses you defined in a slot.

• True
• False

Question 5: We must explicitly enable returns to a slot to continue where we left off before a digression.

• True
• False

Build Your Own Chatbot Final Exam Answers

Question 1: Text and audio are two common ways through which chatbots interact with the user.

• True
• False

Question 2: To be called a chatbot, a bot needs to converse with the user.

• True
• False

Question 3: Chatbots can help cut down the number of inquiries a business’ customer care team needs to manually address.

• True
• False

Question 4: Watson Assistant is a chatbot building service hosted on IBM Cloud (formerly Bluemix).

• True
• False

Question 5: Chatbots can only be deployed on WordPress sites.

• True
• False

Question 6: Which of these is a valid intent?

• @buy_product
• #buy_product
• $buy_product
• #buy product
• @buy product

Question 7: Which of the following are valid system entities?

• @sys-date
• #sys-date
• #sys-person
• @sys-person
• @system-of-a-down

Question 8: The value of an entitity detected in the user input is automatically available for the entire duration of the conversation with the user.

• True
• False

Question 9: We can use context variables to store (and later access) information collected from the user.

• True
• False

Question 10: Slots allow us to collect information from the user and store it in context variables.

• True
• False

Question 11: A dialog has the following nodes: Welcome (welcome condition), Greetings (#greetings intent condition), Thank you (#thank_you intent condition), Goodbyes (#goodbyes intent condition), Anything else (anything_else condition). Which of the following statements apply?

• Welcome will be executed at the beginning of the conversation.
• If no known intent is detected in the user input, the Anything else node will be executed.
• Greetings overshadows Goodbyes, and therefore Goodbyes is never executed.
• The order of Welcome and Anything else in the dialog will generally not matter.
• Rearranging the order to be Welcome, Thank You, Greetings, Goodbyes, Anything else would generally not cause any problems.

Question 12: The evaluation of peer nodes proceeds top to bottom until a node with a matching condition is found. Child nodes are only considered for execution after their parent’s condition is met (or if a jump is involved.)

• True
• False

Question 13: Digressions allow users to divert the conversation away from a node while the slots are being processed (i.e., asking questions to the user).

• True
• False

Question 14: Which of the following contains the user input?

• @sys-person
• text.input
• input.text
• user.text
• None of the above

Question 15: A restaurant chatbot needs to collect the following information from the user: reservation name, party size, date, and time in order to book a reservation. Which of the following approaches works best?

• Have four peer nodes, each asking one of the questions to the user.
• Have a node with multiple slots, each asking for the relevant information. System entities are not needed.
• Have a parent node asking for the reservation name, then a child node asking for party size, then a grandchild node asking for date, then a great-grandchild node asking for the time.
• Have a node with multiple slots, each asking for the relevant information. System entities should be enabled.
• It’s not possible to collect the information for four follow up questions.

Enroll Here: Spark MLlIB Cognitive Class Exam Quiz Answers

Introduction to Spark MLlIB

Apache Spark MLlib (Machine Learning Library) is a scalable machine learning library provided by Apache Spark, which is an open-source cluster computing system. MLlib provides various machine learning algorithms and utilities that simplify the process of building machine learning pipelines.

Key Features of Spark MLlib:

1. Scalability: MLlib is designed to scale out to handle large-scale data processing. It leverages the distributed computing capabilities of Apache Spark, making it suitable for big data analytics.
2. Rich set of Algorithms: MLlib includes a wide range of algorithms and tools for:
   • Classification
   • Regression
   • Clustering
   • Collaborative filtering
   • Dimensionality reduction
   • Feature extraction, transformation, and selection
3. Integration with Spark: MLlib seamlessly integrates with other components of Apache Spark, such as Spark SQL for data manipulation and Spark Streaming for real-time data processing. This integration allows for building end-to-end data pipelines.
4. Ease of Use: It provides high-level APIs in Python, Scala, Java, and R, making it accessible to a broad audience of developers and data scientists. These APIs abstract away the complexities of distributed computing, allowing users to focus on building and deploying machine learning models.
5. Pipeline API: MLlib includes a Pipeline API that facilitates the construction, tuning, and evaluation of machine learning workflows. This API supports feature extraction, transformation, model training, and evaluation in a sequential manner.

Components of Spark MLlib:

• Core Algorithms: Includes fundamental algorithms such as linear regression, logistic regression, decision trees, random forests, k-means clustering, and more.
• Feature Transformers: Tools for feature extraction, transformation (scaling, normalization), and selection (PCA, feature hashing).
• Pipeline: Provides a framework for constructing machine learning pipelines that orchestrate multiple stages of data processing, feature engineering, and model training.
• Persistence: Allows models to be saved and loaded for reuse or deployment in production systems.

Supported Algorithms:

MLlib supports a variety of algorithms across different categories:

• Supervised Learning: Regression, classification, ranking.
• Unsupervised Learning: Clustering, topic modeling.
• Collaborative Filtering: Recommendation systems.
• Dimensionality Reduction: Principal Component Analysis (PCA).

In summary, Apache Spark MLlib is a powerful library for scalable machine learning on big data. It provides a rich set of algorithms, integration with Apache Spark, and ease of use through high-level APIs, making it suitable for a wide range of machine learning applications.

Spark MLlIB Cognitive Class Certification Answers

Module 1 – Spark MLlIB Data Types Quiz Answers

Question 1: Sparse Data generally contains many non-zero values, and few zero values.

• True
• False

Question 2: Local matrices are generally stored in distributed systems and rarely on single machines.

• True
• False

Question 3: Which of the following are distributed matrices?

• Row Matrix
• Column Matrix
• Coordinate Matrix
• Spherical Matrix
• Row Matrix and Coordinate Matrix
• All of the Above

Module 2 – Review Alogrithms Quiz Answers

Question 1: Logistic Regression is an algorithm used for predicting numerical values.

• True
• False

Question 2: The SVM algorithm maximizes the margins between the generated hyperplane and two clusters of data.

• True
• False

Question 3: Which of the following is true about Gaussian Mixture Clustering?

• The closer a data point is to a particular centroid, the more likely that data point is to be clustered with that centroid.
• The Gaussian of a centroid determines the probability that a data point is clustered with that centroid.
• The probability of a data point being clustered with a centroid is a function of distance from the point to the centroid.
• Gaussian Mixture Clustering uses multiple centroids to cluster data points.
• All of the Above

Module 3 – Spark MLlIB Decision Trees and Random Forests Quiz Answers

Question 1: Which of the following is a stopping parameter in a Decision Tree?

• The number of nodes in the tree reaches a specific value.
• The depth of the tree reaches a specific value.
• The breadth of the tree reaches a specific value.
• All of the Above

Question 2: When using a regression type of Decision Tree or Random Forest, the value for impurity can be measured as either ‘entropy’ or ‘variance’.

• True
• False

Question 3: In a Random Forest, featureSubsetStrategy is considered a stopping parameter, but not a tunable parameter.

• True
• False

Module 4 – Spark MLlIB Clustering Quiz Answers

Question 1: In Spark MLlib, the initialization mode for the K-Means training method is called

• k-means–
• k-means++
• k-means||
• k-means

Question 2: In K-Means, the “runs” parameter determines the number of data points allowed in each cluster.

• True
• False

Question 3: In Gaussian Mixture Clustering, the sum of all values outputted from the “weights” function must equal 1.

• True
• False

Spark MLlIB Final Exam Answers

Question 1: In Gaussian Mixture Clustering, the predictSoft function provides membership values from the top three Gaussians only.

• True
• False

Question 2: In Decision Trees, what is true about the size of a dataset?

• Large datasets create “bins” on splits, which can be specified with the maxBins parameter.
• Large datasets sort feature values, then use the ordered values as split calculations.
• Small datasets create split candidates based on quantile calculations on a sample of the data.
• Small datasets split on random values for the feature.

Question 3: A Logistic Regression algorithm is ineffective as a binary response predictor.

• True
• False

Question 4: What is the Row Pointer for a Matrix with the following Row Indices: [5, 1 | 6 | 2, 8, 10]

• [1, 6]
• [0, 2, 3, 6]
• [0, 2, 3, 5]
• [2, 3]

Question 5: For multiclass classification, try to use (M-1) Decision Tree split candidates whenever possible.

• True
• False

Question 6: In a Decision Tree, choosing a very large maxDepth value can:

• Increase accuracy
• Increase the risk of overfitting to the training set
• Increase the cost of training
• All of the Above
• Increase the risk of overfitting and increase the cost of training

Question 7: In Gaussian Mixture Clustering, a large value returned from the weights function represents a large precedence of that Gaussian.

• True
• False

Question 8: Increasing the value of epsilon when creating the K-Means Clustering model can:

• Decrease training cost and decrease the number of iterations that the model undergoes
• Decrease training cost and increase the number of iterations that the model undergoes
• Increase training cost and decrease the number of iterations that the model undergoes
• Increase training cost and increase the number of iterations that the model undergoes

Question 9: In order to train a machine learning model in Spark MLlib, the dataset must be in the form of a(n)

• Python List
• Textfile
• CSV file
• RDD

Question 10: What is true about Dense and Sparse Vectors?

• A Dense Vector can be created using a csc_matrix, and a Sparse Vector can be created using a Python List.
• A Dense Vector can be created using a SciPy csc_matrix, and a Sparse Vector can be created using a SciPy NumPy Array.
• A Dense Vector can be created using a Python List, and a Sparse Vector can be created using a SciPy csc_matrix.
• A Dense Vector can be created using a SciPy NumPy Array, and a Sparse Vector can be created using a Python List.

Question 11: In a Decision Tree, increaing the maxBins parameter allows for more splitting candidates.

• True
• False

Question 12: In classification models, the value for the numClasses parameter does not depend on the data, and can change to increase model accuracy.

• True
• False

Question 13: What is true about Labeled Points?

• A – A labeled point is used with supervised machine learning, and can be made using a dense local vector.
• B – A labeled point is used with unsupervised machine learning, and can be made using a dense local vector.
• C – A labeled point is used with supervised machine learning, and can be made using a sparse local vector.
• D – A labeled point is used with unsupervised machine learning, and can be made using a sparse local vector
• All of the Above
• A and C only

Question 14: In the Gaussian Mixture Clustering model, the convergenceTol value is a stopping parameter that can be tuned, similar to epsilon in k-means clustering.

• True
• False

Question 15: In Gaussian Mixture Clustering, the “Gaussians” function outputs the coordinates of the largest Gaussian, as well as the standard deviation for each Gaussian in the mixture.

• True
• False

Question 16: What is true about the maxDepth parameter for Random Forests?

• A large maxDepth value is preferred since tree averaging yields a decrease in overall bias.
• A large maxDepth value is preferred since tree averaging yields a decrease in overall variance.
• A large maxDepth value is preferred since tree averaging yields an increase in overall bias.
• A large maxDepth value is preferred since tree averaging yields an increase in overall variance.

Enroll Here: Big Data 101 Cognitive Class Exam Quiz Answers

Introduction to Big Data 101

Big Data refers to large and complex datasets that are difficult to process using traditional data management and processing techniques. The term encompasses not only the size of the data but also its variety (different types of data), velocity (speed at which data is generated and processed), and veracity (uncertainty of data).

The emergence of Big Data is primarily driven by the vast amounts of information generated by digital technologies such as social media, sensors, mobile devices, and other sources. These datasets are typically too large to be handled by traditional database systems or software tools, which were designed for smaller-scale data processing.

Key characteristics of Big Data include:

1. Volume: The sheer amount of data generated every second is immense and continues to grow exponentially.
2. Variety: Data comes in various forms including structured data (e.g., databases), semi-structured data (e.g., XML, JSON), and unstructured data (e.g., text, multimedia files).
3. Velocity: Data is generated and needs to be processed at high speed. Real-time or near-real-time processing is often required to extract value from data streams.
4. Veracity: Refers to the quality and reliability of the data. Big Data often includes data from unreliable or uncertain sources, requiring careful validation and cleansing.

To handle Big Data effectively, specialized tools and technologies have been developed, such as distributed computing frameworks like Hadoop and Spark, NoSQL databases, and data lakes. These technologies allow organizations to store, manage, and analyze large volumes of data to extract valuable insights, make data-driven decisions, and gain competitive advantages.

In summary, Big Data represents a paradigm shift in data management and analysis, driven by the exponential growth in data volume, variety, velocity, and the need to derive actionable insights from diverse and rapidly changing data sources.

Big Data 101 Cognitive Class Certification Answers

Module 1 – What is Big Data Quiz Answers

Question 1: Name one of the drivers of Volume in the Big Data Era?

• Scalable infrastructure
• Cost
• Competitive advantage
• FinTech
• Research and development

Question 2: Value from Big Data can be _______________?

• Profits
• Veracity
• Petabytes
• Technical ability
• Infrastructure

Question 3: In the video, 2.5 Quintillion Bytes of data are equivalent to how many blue ray DVDs?

• 1 Billion
• 10 million
• 100 million
• 5 million
• 1 Trillion

Module 2 – Big Data – Beyond the Hype Quiz Answers

Question 1: How many petabytes make up an Exabyte

• 32
• 2020
• 64
• 1024
• 8

Question 2: What is an example of a source of Semi-Structured Big data?

• Cameras files
• Relational databases
• Satellite files
• Spreadsheet file
• JSON files

Question 3: When is it estimated that the data we create and copy will reach around 35 zettabytes?

• We have already surpassed this mark
• 2050
• 2030
• 2040
• 2020

Module 3 – The Big Data and Data Science Quiz Answers

Question 1: What is the process of cleaning and analyzing data to derive insights and value from it?

• Machine Learning
• Exploratory Research
• Data Science
• Predictive Modeling
• Decision Trees

Question 2: What is the search engine used by Walmart?

• JSON
• HBase
• ZooKeeper
• Polaris
• Poisson

Question 3: An example of visualizing Big Data is___________?

• Hadoop
• Integration
• Agile Governance
• Temperature on a map
• Closing your eyes and imagining it

Module 4 – Big Data Use Cases Quiz Answers

Question 1: What is the term used to describe an holistic approach that takes into account all available and meaningful information about a customer to drive better engagement, revenue and long term loyalty?

• Enhanced 360-degree view
• Big Data Exploration
• End to End
• Operations Analysis
• Customer Retention

Question 2: What can help organizations to find new associations or uncover patterns and facts to significantly improve intelligence, security and law enforcement?

• Using local servers
• Analyzing data in-motion and at rest
• Satellite data
• GPS coordinates
• Using XML

Question 3: In Operations Analysis, we focus on what type of data?

• Location Data
• Machine Data
• Binary Data
• Social Media Data
• Structured Data

Module 5 – Processing Big Data Quiz Answers

Question 1: What is a method of storing data to support the analysis of originally disparate sources of data?

• Data Lakes
• Data Mining
• Predictive Analytics
• Data Analytics
• Deep Learning

Question 2: Data Warehouses provide online analytic processing: True/False

• False
• True

Question 3: What does ‘OLAP’ stand for?

• Online Analytical Prediction
• Online Analytical Platform
• Online Analytical Processing
• Online Advanced Prediction
• Online Advanced Programming

Big Data Final Exam Answers

Question 1: In Module 1: What is a common use of big data that is used by companies like Netflix, Spotify, Facebook and Amazon?

• Recommendation Engines
• Data Lakes
• Clusters
• The Cloud
• Sensors

Question 2: In Module 2: Is one byte binary? True/False

• False
• True

Question 3: In Module 2: What has highly contributed to the launch of the Big Data era?

• Clusters
• Spark
• Cloud Computing
• Zetabytes
• Data Scientists

Question 4: In Module 3: A data scientist is a person who is qualified to derive insights from data by using skills and experience from computer science, business or science, and statistics. True/False

• False
• True

Question 5: In Module 3: ‘HDFS’ stands for ____________________?

• Hadoop Data Fraud System
• High Data File System
• Hadoop Distributed File System
• High Distribution Frequency System
• High Definition Frequency Sensors

Question 6: In Module 3: Data privacy is a critical part of the big data era. Businesses and individuals must give great thought to how data is _____________________________.

• collected, retained, used, and disclosed
• bought, sold, stored and analyzed
• secured, sold, downloaded and uploaded
• aggregated, compiled, saved and stored
• stored, analyzed, read and written

Question 7: In Module 5: In the Hadoop framework, a rack is a collection of ____________?

• Yarn
• Networks
• Bits
• Nodes
• Distributed files

Question 8: In Module 5: What is a method of storing data to support the analysis of originally disparate sources of data?

• Spark
• Data Warehouse
• Yarn
• Data Repository
• Data Lake

Question 9: In Module 5: The Hadoop framework is mostly written in the Java programming language. True/False

• False
• True

Question 10: In Module 5: What is the term referring to a database that must be processed by means other than just the SQL Query Language.

• Spark
• NoSQL
• Python
• SQL
• Hadoop

Enroll Here: Hadoop 101 Cognitive Class Exam Quiz Answers

Introduction to Hadoop 101

Hadoop is a powerful framework designed for distributed storage and processing of large data sets across clusters of computers using simple programming models. It is open-source and part of the Apache project, maintained and developed by a global community of contributors.

Key Components of Hadoop:

1. Hadoop Distributed File System (HDFS):
   • HDFS is a distributed file system that stores data across multiple machines without requiring them to be in close proximity. It provides high-throughput access to application data and is fault-tolerant.
2. Yet Another Resource Negotiator (YARN):
   • YARN is a framework responsible for managing computing resources in clusters and scheduling users’ applications. It separates the resource management and job scheduling/monitoring functionalities, allowing for more efficient resource utilization.
3. MapReduce:
   • MapReduce is a programming model and processing engine for distributed computing on large data sets. It consists of two main functions: Map, which processes data in parallel across multiple nodes, and Reduce, which aggregates the results of the Map step.

Benefits of Hadoop:

• Scalability: Hadoop can scale from single servers to thousands of machines, each offering local computation and storage.
• Reliability: It automatically handles hardware failures and ensures data availability.
• Flexibility: It can handle various types of data, structured and unstructured, and supports multiple programming languages.
• Cost-effective: Uses commodity hardware to store large quantities of data.

Ecosystem:

Hadoop has a rich ecosystem with additional components and tools that extend its capabilities, such as Apache Hive (data warehouse infrastructure), Apache Pig (platform for analyzing large data sets), Apache HBase (distributed NoSQL database), and many others.

Conclusion:

Hadoop revolutionized the way large-scale data processing is handled by providing an efficient, scalable, and cost-effective framework. It continues to evolve, addressing new challenges in the field of big data analytics and processing.

Hadoop 101 Cognitive Class Certification Answers

Module 1 – Introduction to Hadoop Quiz Answers

Question 1: Hadoop is designed for Online Transactional Processing. True or False?

• True
• False

Question 2: When is Hadoop useful for an application?

• When all of the application data is unstructured
• When work can be parallelized
• When the application requires low latency data access
• When random data access is required

Question 3: With the help of InfoSphere Streams, Hadoop can be used with data-at-rest as well as data-in-motion. True or false?

• True
• False

Module 2 – Hadoop Architecture & HDFS Quiz Answers

Question 1: Network bandwidth between any two nodes in the same rack is greater than bandwidth between two nodes on different racks. True or False?

• True
• False

Question 2: Hadoop works best on a large data set. True or False?

• True
• False

Question 3: HDFS is a fully POSIX compliant file system. True or False?

• True
• False

Module 3 – Hadoop Administration Quiz Answers

Question 1: You can add or remove nodes from the open source Apache Ambari console. True or False?

• True
• False

Question 2: It is recommended that you start all of the services in Ambari in order to speed up communications. True or False?

• True
• False

Question 3: To remove a node using Ambari, you must first remove all of the services using that node. True or False?

• True
• False

Module 4 – Hadoop Components Quiz Answers

Question 1: The output of the shuffle operation goes into the mapper before going into the reducer. True or False?

• True
• False

Question 2: What is true about Pig and Hive in relation to the Hadoop ecosystem?

• HiveQL requires that you create the data flow
• PigLatin requires that the data have a schema
• Fewer lines of code are required compared to a Java program
• All of the above

Question 3: Which of the following tools is designed to move data to and from a relational database?

• Pig
• Flume
• Oozie
• Sqoop

Hadoop 101 Final Exam Answers

Question 1: HDFS is designed for:

• Large files, streaming data access, and commodity hardware
• Large files, low latency data access, and commodity hardware
• Large files, streaming data access, and high-end hardware
• Small files, streaming data access, and commodity hardware
• None of the options is correct

Question 2: The Hadoop distributed file system (HDFS) is the only distributed file system supported by Hadoop. True or false?

• True
• False

Question 3: The input to a mapper takes the form < k1, v1 > . What form does the mapper’s output take?

• < list(k2), v2 >
• list( < k2, v2 > )
• < k2, list(v2) >
• < k1, v1 >
• None of the options is correct

Question 4: What is Flume?

• A service for moving large amounts of data around a cluster soon after the data is produced.
• A distributed file system.
• A programming language that translates high-level queries into map tasks and reduce tasks.
• A platform for executing MapReduce jobs.
• None of the options is correct

Question 5: What is the purpose of the shuffle operation in Hadoop MapReduce?

• To pre-sort the data before it enters each mapper node.
• To distribute input splits among mapper nodes.
• To transfer each mapper’s output to the appropriate reducer node based on a partitioning function.
• To randomly distribute mapper output among reducer nodes.
• None of the options is correct

Question 6: Which of the following is a duty of the DataNodes in HDFS?

• Control the execution of an individual map task or a reduce task.
• Maintain the file system tree and metadata for all files and directories.
• Manage the file system namespace.
• Store and retrieve blocks when told to by clients or the NameNode.
• None of the options is correct

Question 7: Which of the following is a duty of the NameNode in HDFS?

• Control the MapReduce job from end-to-end
• Maintain the file system tree and metadata for all files and directories
• Store the block data
• Transfer block data from the data nodes to the clients
• None of the options is correct

Question 8: Which component determines the specific nodes that a MapReduce task will run on?

• The NameNode
• The JobTracker
• The TaskTrackers
• The JobClient
• None of the options is correct

Question 9: Which of the following characteristics is common to Pig, Hive, and Jaql?

• All translate high-level languages to MapReduce jobs
• All operate on JSON data structures
• All are data flow languages
• All support random reads/writes
• None of the options is correct

Question 10: Which of the following is NOT an open source project related to Hadoop?

• Pig
• UIMA
• Jackal
• Avro
• Lucene

Question 11: During the replication process, a block of data is written to all specified DataNodes in parallel. True or false?

• True
• False

Question 12: With IBM BigInsights, Hadoop components can be started and stopped from a command line and from the Ambari Console. True or false?

• True
• False

Question 13: When loading data into HDFS, data is held at the NameNode until the block is filled and then the data is sent to a DataNode. True or false?

• True
• False

Question 14: Which of the following is true about the Hadoop federation?

• Uses JournalNodes to decide the active NameNode
• Allows non-Hadoop programs to access data in HDFS
• Allows multiple NameNodes with their own namespaces to share a pool of DataNodes
• Implements a resource manager external to all Hadoop frameworks

Question 15: Which of the following is true about Hadoop high availability?

• Uses JournalNodes to decide the active NameNode
• Allows non-Hadoop programs to access data in HDFS
• Allows multiple NameNodes with their own namespaces to share a pool of DataNodes
• Implements a resource manager external to all Hadoop frameworks

Question 16: Which of the following is true about YARN?

• Uses JournalNodes to decide the active NameNode
• Allows non-Hadoop programs to access data in HDFS
• Allows multiple NameNodes with their own namespaces to share a pool of DataNodes
• Implements a resource manager external to all Hadoop frameworks

Question 17: Which of the following sentences is true?

• Hadoop is good for OLTP, DSS, and big data
• Hadoop includes open-source components and closed source components
• Hadoop is a new technology designed to replace relational databases
• All of the options are correct
• None of the options is correct

Question 18: In which of these scenarios should Hadoop be used?

• Processing billions of email messages to perform text analytics
• Obtaining stock price trends on a per-minute basis
• Processing weather sensor information to predict a hurricane path
• Analyzing vital signs of a baby in real time
• None of the options is correct

Enroll Here: Statistics 101 Cognitive Class Exam Quiz Answers

Introduction to Statistics 101

Statistics is the science of collecting, analyzing, interpreting, and presenting data. It plays a crucial role in understanding patterns and making informed decisions in various fields, from science and medicine to business and social sciences. Here are some fundamental concepts in statistics:

1. Data: Statistics begins with data, which can be numerical or categorical information collected from observations or experiments.
2. Descriptive Statistics: These methods are used to summarize and describe features of the data through measures such as averages (mean, median, mode), variability (range, variance, standard deviation), and distribution (skewness, kurtosis).
3. Inferential Statistics: This involves making predictions or generalizations about a population based on a sample of data. It includes techniques like hypothesis testing, confidence intervals, and regression analysis.
4. Probability: Probability theory is fundamental to statistics, dealing with the likelihood of events occurring in a random phenomenon. It provides the theoretical foundation for statistical methods.
5. Statistical Methods: There are various methods for analyzing data, including:
   • Parametric Methods: Assumes data follows a specific distribution (e.g., normal distribution) and uses parameters to describe it.
   • Non-parametric Methods: Does not assume data follows a particular distribution and is used when assumptions of parametric methods are not met.
   • Multivariate Analysis: Techniques that analyze relationships between multiple variables simultaneously.
   • Bayesian Methods: Utilizes Bayes’ theorem to update beliefs about the likelihood of events based on new evidence.
6. Applications: Statistics is applied in diverse fields such as economics, sociology, psychology, biology, engineering, and more. It helps in decision-making, research, quality control, forecasting, and policy analysis.

Understanding statistics is essential for critically evaluating information, designing experiments, and drawing reliable conclusions from data. It provides powerful tools for uncovering patterns, testing hypotheses, and making data-driven decisions in a complex world.

Statistics 101 Cognitive Class Certification Answers

Module 1 – Welcome to Statistics Quiz Answers

Question 1: Which one of the following is not an example of statistics?

• The sweet smell of success
• Monthly housing prices in a city
• Traffic noise at a busy intersection
• Annual unemployment rate in a country

Question 2: Which of the following statements is true? One can estimate the votes for a presidential candidate in a forthcoming election by:

• Asking your barber
• Conducting a poll of a random sample of the voting age population
• Asking your favourite university professor about who is going to win
• Asking the cab drivers in a city of their vote preference

Question 3: Which of the following is not a type of data visualization? (Pick the most appropriate answer)

• An organization chart
• A pie chart
• A time series plot
• A bar chart

Module 2 – Descriptive Statistics Quiz Answers

Question 1: Which of the following is not a cross-sectional data set?

• Monthly survey of consumer confidence
• National Census conducted every 5 or 10 years
• Weekly data on average temperature
• A survey of student satisfaction conducted at the end of the course

Question 2: Which of the following is an example of time series data?

• Number of dolphins in the Pacific Ocean
• Average batting average of a baseball player
• Number of trees in Jardin du Luxemburg in Paris
• Annual average housing price in New York

Question 3: Which of the following is an example of multivariate data?

• Vital signs recorded for a new born baby
• Number of songs played in a day by your favourite radio station
• Daily temperature recorded by a monitoring station in Antarctica
• Number of words spoken by President Donald Trump in his inaugural speech

Module 3 – Advanced Descriptive Statistics Quiz Answers

Question 1: What is a suitable way to display the average income earned by men and women in a city?

• A scatter plot
• A pie chart
• A histogram
• A bar chart

Question 2: What is a suitable way to display relationship between two continuous variables?

• A scatter plot
• A pie chart
• A histogram
• A bar chart

Question 3: What’s the best way to display median and outliers?

• A bubble chart
• A time series plot
• A box plot
• A scatter plot

Module 4 – Visualization Quiz Answers

Question 1: What is the best way to display daily temperature for a city?

• A histogram
• A pie chart
• A Box plot
• A line plot

Question 2: What extra step is needed to display two related time series variables that differ greatly in magnitude?

• Use two axes to display the lines
• Plot them by colouring the lines with different colours
• Plot the lines with different thickness
• Plot them separately in two charts

Question 3: When the sum of two or more categories equals 100, what chart type is ideally suited for displaying data?

• A line chart
• A pie chart
• A box plot
• A histogram

Module 5 – “From Start to Finish: Beauty Pays Data” Quiz Answers

Question 1: When using sample data with weights, it is important to compute statistics by:

• Filtering the data with the weight variable
• Weighting the data with the appropriate variable
• Ignoring the weights
• None of the above

Question 2: When multiple observations are reported for each respondent in the data set, to compute statistics for variables about the respondents, one must:

• Ignore the presence of duplicates and compute statistics as usual
• Weight data by duplicates
• Remove duplicates before running analysis
• None of the above

Question 3: To be able to trace one’s steps, one must:

• Generate and record syntax for every command executed for the analysis
• Note steps taken for the analyses in a notebook
• Use mouse for point and click to undertake the analysis
• None of the above

Statistics 101 Final Exam Answers

Question 1: What is meta data?

• Data about metal fatigue
• The metabolism data in a clinical trial
• The data about metamorphism
• It’s the data about data

Question 2: Which of the following is not an example of big data?

• Number of photographs uploaded to the internet every day
• The emails sent daily from your email provider
• The number of big basketball players in NBA (National Basketball Association)
• Weekly data about individual credit card transactions registered for your local credit card company

Question 3: SPSS is ideally suited to analyze data stored in:

• Books as words and paragraphs
• Digital video files of Hollywood movies
• Tables as rows and columns
• Digital audio files of music records

Question 4: Reproducibility in statistical analysis requires one to use statistical software that supports:

• Free usage for analysis
• Syntax (script) based analysis
• Tabular output of results
• A point and click environment

Question 5: Which of the following is an example of categorical data?

• Number of fire hydrants in a city
• Number of children at a kindergarten
• Length of the river Nile
• Mode of travel to work

Question 6: Which of the following is not an example of ordinal data?

• Ranking of athletes in an Olympic competition
• Number of trees in a park
• Level of happiness on a scale of 1 to 5
• Street numbers

Question 7: Which of the following is an example of interval data?

• The ethnicity of a person
• “None”, “Some”, “Frequent” – representing the frequency of exercise
• First, second and third rankings in a sports competition
• Weight

Question 8: For a survey of student satisfaction in a course, the population comprises:

• All students enrolled in the course
• All male students registered in the department
• All A+ students enrolled in the course
• All students registered at the university

Question 9: A mean is meaningful for the following type of data

• Audio data
• Ordinal data
• Ratio data
• Categorical data

Question 10: Median represents a value in the data set where:

• Half of the observations are above the median and the other half below it
• Most observations are negative
• Half of the observations are known and the other half not known
• Most observations are positive

Question 11: If the standard deviation of a variable is larger than the mean, the variable depicts:

• Fluidity
• Low variance
• Smoothness
• High variance

Question 12: A histogram is a graphical display of how a variable is

• Observed
• Displayed
• Distributed
• Recorded

Question 13: The following type of computation is suited for categorical data:

• Proportions
• Standard deviations
• Histogram
• Averages

Question 14: The relationship between two categorical variables can be captured by:

• Standard deviation
• A crosstabulation
• A bar chart
• A histogram

Question 15: The probability of getting a 2 by rolling TWO six-sided dice (with sides labeled as 1, 2, 3, 4, 5, 6) is

• 1/36
• 1/18
• 2
• 2/36

Question 16: What is the best way to determine the significance of relationship between two categorical variables?

• A regression model
• A Pearson Correlation test
• A Chi-square test
• A t-test

Question 17: If two continuous variables are positively correlated, their scatter plot will depict:

• A flat line
• A downward sloping curve
• An upward sloping curve
• None of the above

Question 18: What is the best way to determine the significance of relationship between two continuous variables?

• A regression model
• A Pearson Correlation test
• A Chi-square test
• A t-test

Question 19: A good chart should not be missing the following:

• A self-explanatory variable title
• Thick borders
• A dark background colour
• Bright colours

Question 20: What is the best practice to display axes labels?

• Use self-explanatory variables
• Use variable names
• Use bold font to highlight labels
• Don’t use any labels

Enroll Here: Solr 101 Cognitive Class Exam Quiz Answers

Introduction to Solr 101

Apache Solr is an open-source search platform built on Apache Lucene. It’s designed for scalability and provides powerful full-text search, hit highlighting, faceted search, dynamic clustering, database integration, and rich document handling. Solr is widely used for building enterprise search applications and powering e-commerce search, content repositories, and other applications requiring fast and efficient search functionality.

Key Features of Apache Solr:

1. Full-Text Search: Solr supports full-text search capabilities, enabling users to perform complex queries on large volumes of data.
2. Scalability and Performance: Solr is designed to scale horizontally, allowing you to distribute indexing and search across multiple servers for high availability and performance.
3. Faceted Search: Also known as faceted navigation or faceted browsing, this feature allows users to filter search results by various criteria, such as categories, tags, or metadata.
4. Highlighting: Solr can highlight search terms within the retrieved documents, making it easier for users to identify relevant information.
5. Geospatial Search: Solr provides support for geospatial search queries, allowing users to search for documents based on location or spatial proximity.
6. NoSQL Features: Solr can function as a NoSQL database, supporting features like schemaless mode, JSON support, and document indexing.
7. Integration: It integrates well with other systems and databases, supporting data import/export features and offering connectors for popular data sources.
8. Extensibility: Solr is highly extensible through plugins and customizations, allowing developers to tailor it to specific use cases.

Components of Apache Solr:

• Indexing: Solr indexes documents that you want to search. It supports various document formats like XML, JSON, PDF, Word, and more.
• Querying: Solr provides a powerful query language for retrieving indexed documents based on user queries. Queries can be simple keyword searches or complex queries with filters, facets, and more.
• Administration UI: Solr comes with a built-in web interface (Solr Admin UI) for managing and monitoring Solr instances, configuring settings, and analyzing performance.
• Integration: Solr can be integrated into applications via its REST-like HTTP/XML and JSON APIs, making it accessible from different programming languages and platforms.

Getting Started with Apache Solr:

To get started with Apache Solr, you typically:

1. Download and Install: Download Solr from the official Apache Solr website and install it on your server or local machine.
2. Index Data: Index your documents into Solr. You can use Solr’s Data Import Handler (DIH), HTTP POST requests, or other methods to populate your Solr index.
3. Query Data: Use Solr’s query syntax to retrieve documents based on user queries. Experiment with filters, facets, highlighting, and other features to enhance search capabilities.
4. Monitor and Tune: Monitor the performance of your Solr instance using Solr Admin UI or monitoring tools. Tune Solr configuration based on your performance requirements.
5. Integrate: Integrate Solr into your application by sending HTTP requests to Solr’s APIs to perform searches and retrieve search results.

Apache Solr provides robust search capabilities that can be tailored to meet diverse search requirements, from small-scale projects to large enterprise applications. It’s a versatile tool that continues to evolve with new features and improvements to support modern search and information retrieval needs.

Solr 101 Cognitive Class Certification Answers

Module 1 – Search Engines Quiz Answers

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

• Solr is written in C# and built around the Apache Lucene search engine.
• When you perform a search in Solr, the index gets searched.
• Solr is a web application running on its own server.
• Solr uses an inverted index that maps tokens to locations.

Question 2: Queries can be analyzed before they are submitted to Solr. True or false?

• False
• True

Question 3: Which of the following options is NOT correct?

• You don’t explicitly have to tell Solr how to read csv files.
• The index in Solr is composed of many documents.
• Each document is composed of many fields.
• Solr handles unstructured documents automatically.

Module 2 – Configuring and Adding Documents to Solr Quiz Answers

Question 1: Which file specifies how documents are organized in the index?

• solrconfig.xml
• schema.json
• schema.dat
• schema.xml

Question 2: Fields must be indexed and stored. True or false?

• False
• True

Question 3: Which of the following methods can be used to add a document to Solr?

• Use a client API such as SolrJ.
• Use a data import handler.
• Create a Solr Cell to extract text from formats like PDFs and Word documents.
• Directly make a request to the update URL.
• All of the above

Module 3 – Analyzers Queries Quiz Answers

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

• StopFilter removes generally useless words such as “the”, “at”, “and”.
• Analyzers consist of tokenizers and filters.
• StandardTokenizer delimits by whitespace and punctuation, except for periods not followed by whitespace.
• Tokenizers and filters can only be chained together in a specific order.

Question 2: Lowercase filter converts all letters in each token to lowercase without touching non-letters. True or false?

• False
• True

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

• Square brackets make the range exclusive, and curly braces are inclusive.
• You can make a query negative by adding a minus symbol prefix.
• You can match multiple values against the same field in a single term.
• If the search string is an asterisk, you won’t necessarily get every document.

Module 4 – Solrj and Customization Quiz Answers

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

• SolrJ is the Java client API for Solr.
• SolrJ is not as powerful as update processors and request handlers.
• SolrJ does not manage the configuration of your server.
• SolrJ eliminates the need to manually construct HTTP requests and handle responses.

Question 2: Which location does NOT contain SolrJ dependencies by default?

• solr/example/dist
• solr/dist/solrj-lib
• solr/example/lib/ext
• solr/dist

Question 3: SolrCloud is managed by Apache Zookeeper. True or false?

• False
• True

Solr 101 Final Exam Answers

Question 1: The data model in Solr consists of

• Database, Storage, Data
• Index, Row, Column
• Index, Document, Field
• None of the above

Question 2: You have the following field in one of your documents:

title : “Game of Thrones”

The field is defined as follows:

< field name=”title” type=”string” indexed=”true” / >

Which of the following query strings would match the title?

• “Game of Thrones”
• “Thrones”
• “game of thrones”
• All of the above

Question 3: Where are document fields defined?

• Solr web console
• schema.xml
• solrconfig.xml
• None of the above

Question 4: The first step of many Solr projects is to make a copy of the Solr example directory. True or false?

• True
• False

Question 5: The embedded zookeeper server available with Solr is suitable for production. True or false?

• True
• False

Question 6: The best way to index a SQL database is

• Manually
• Apache Tika Extractor
• SolrJ
• Data Import Handler

Question 7: If you want the “title” field of your documents to be searchable, which attribute is necessary?

• indexed=”false”
• stored=”false”
• indexed=”true”
• stored=”true”

Question 8: Where do you specify the set of analyzers that a field will use?

• solrconfig.xml
• The fieldType in schema.xml
• The field in schema.xml
• None of the above

Question 9: Solr is a web server that communicates via HTTP. True or false?

• True
• False

Question 10: How would you configure Solr to automatically generate an “id” field for each document?

• Set primaryKey=”true” in the document field
• Add an Update Request Processor to solrconfig.xml
• No configuration needed – Solr will fill in missing fields by default
• This is not possible

Question 11: How can you add documents to Solr?

• With a client API
• With a Data Import Handler
• Extract with Apache Tika
• Directly via URL
• All of the above

Question 12: Where do you put queries in a Data Import Handler?

• Source tag
• Query tag
• Document tag
• Entity tag

Question 13: You can join a nested entity with its parent entity, just like a SQL JOIN statement. True or false?

• True
• False

Question 14: When you extract from a PDF or Word Document, all of the text is put into a single field called “content”. True or false?

• True
• False

Question 15: How would you search for all documents with a quantity greater than 20 but less than 50?

• quantity:[BETWEEN 20 AND 50]
• quantity:{20 TO 50}
• 20 < QUANTITY > 50
• quantity:[20 TO 50]

Question 16: What is the meaning of the ^ query operator?

• ^ is the “boost” part of the query, increasing its score
• ^ is the Boolean OR operator
• ^ is the Boolean AND operator
• ^ is a wildcard character that matches anything

Question 17: When using a client API like SolrJ, you don’t need to configure schema.xml and solrconfig.xml. True or false?

• True
• False

Question 18: Using SolrJ, how would you connect to collection 1 on your Solr server?

• solr = new HttpSolrServer();
• solr = new HttpSolrServer(“http://localhost:8983/solr”, “collection1” );
• HttpSolrServer.connect(“collection1”);
• solr = new HttpSolrServer(“http://localhost:8983/solr/collection1” );

Question 19: In SolrJ, how do you finalize changes to your index?

• solr.commit();
• solr.end();
• solr.finish();
• None of the above

Question 20: What is a facet query?

• A separate query that provides a summary of data.
• A query run on a subset of the index
• A secondary query that restricts the results of the original query
• None of the above

Enroll Here: Apache Pig 101 Cognitive Class Exam Quiz Answers

Introduction to Apache Pig 101

Apache Pig is a high-level platform for processing and analyzing large datasets on Apache Hadoop. It provides a high-level language called Pig Latin, which is used to express data analysis programs. Pig Latin abstracts the complexities of MapReduce programming, making it easier for developers to write data processing pipelines without diving into the intricacies of MapReduce code.

Key Features of Apache Pig:

1. Ease of Use: Pig Latin is designed to be easy to write and understand, especially for those familiar with SQL or scripting languages. It allows developers to focus more on the data flow and transformations rather than low-level programming.
2. Extensibility: Pig provides a rich set of built-in operators for data operations like joins, filters, grouping, and sorting. Additionally, developers can extend Pig by writing User-Defined Functions (UDFs) in Java, Python, or other languages.
3. Optimization: Pig optimizes the data flow automatically, converting Pig Latin scripts into sequences of MapReduce jobs. It applies optimization techniques to improve performance, such as query optimization and execution plans.
4. Integration: Pig integrates seamlessly with other components of the Apache Hadoop ecosystem, such as HDFS (Hadoop Distributed File System) for storage and YARN for resource management.

How Apache Pig Works:

• Script Execution: Developers write Pig scripts in Pig Latin, which describe the data transformations and operations. These scripts are then executed using the Pig runtime environment.
• MapReduce Execution: Internally, Pig translates these scripts into a series of MapReduce jobs, which are executed on the Hadoop cluster. Each operation in Pig Latin corresponds to one or more MapReduce jobs.
• Data Flow: Pig processes data in a pipeline fashion, where each operation takes input data, performs transformations, and produces output that serves as input to the next operation.

In summary, Apache Pig simplifies the development of data processing applications on Hadoop by providing a higher-level abstraction through its Pig Latin language. It is particularly suited for scenarios where complex data transformations and analyses need to be performed on large-scale datasets efficiently.

Apache Pig 101 Cognitive Class Certification Answers

Module 1: Pig Basics Quiz Answers

Question 1: What are the five ways to invoke Pig?

• Script, Interactive Mode, Java Command, Interactive Local Mode, Interactive MapReduce Mode
• Interactive External Mode, Interactive Mode, Script, Java Command, Interactive MapReduce Mode
• Interactive Service Mode, Interactive Local Mode, Interactive External Mode, Interactive MapReduce Mode, Java Command
• Interactive Local Mode, Interactive MapReduce Mode, Interactive External Mode, Interactive Mode, Script

Question 2: Bags are groups of tuples, tuples are groups of fields, and fields are composed of scalar data types. True or false?

• True
• False

Question 3: Which of the following statements is true?

• Names of relations and fields, as well as keywords and operators, are case sensitive. However, function names are case insensitive.
• Keywords and operator names are case sensitive.
• Function names are case sensitive.
• Names of relations are case sensitive, but names of fields are case insensitive.

Module 2: Pig Relational Operators Quiz Answers

Question 1: For the tuples (3,5,2) (5,2,1) (3,7,3) (3,6,1), using the GROUP operator on the third field produces the following: (2,{(3,5,2)}), (1,{(5,2,1),(3,6,1)}), (3,{(3,7,3)}). True or false? Disregard order when answering.

• True
• False

Question 2: UNION, GROUP, and COGROUP can be used interchangeably without creating different outputs. True or false?

• True
• False

Question 3: Which operators can be used within a nested FOREACH block?

• LIKE, COUNT, LIMIT, ORDER BY
• COUNT, ORDER BY, AVG, DISTINCT
• AVG, LIMIT, FILTER, LIKE
• LIMIT, DISTINCT, ORDER BY, FILTER

Module 3: Pig Evolution Function Quiz Answers

Question 1: The COUNT operator does NOT require the use of the GROUP BY operator. True or false?

• True
• False

Question 2: The TOKENIZE() function splits a string and outputs a bag of words. True or false?

• True
• False

Question 3: The two types of UDFs are DEFINE and REGISTER. True or false?

• True
• False

Apache Pig 101 Final Exam Answers

Question 1: What is the primary purpose of Pig in the Hadoop architecture?

• To provide logging support for Hadoop jobs
• To support the execution of workflows consisting of a collection of actions
• To provide a high-level programming language so that developers can simplify the task of writing MapReduce applications
• To move data into HDFS

Question 2: When executing Pig in local mode, the process runs locally, but all of the data files are accessed via HDFS. True or false?

• True
• False

Question 3: Data can be loaded into Pig with or without defining a schema. True or false?

• True
• False

Question 4: In Pig, you can specify the delimiter used to load data by

• doing nothing. Pig can automatically detect the delimiter used in your data file
• adding a schema definition to your LOAD statement
• adding ‘using PigStorage(delimiter)’ to your LOAD statement
• All of the above

Question 5: Which of the following can be used to pass parameters into a Pig Script? Select all that apply.

• Command line parameters
• A parameter files
• JSON
• Web Services

Question 6: Which Pig Operator is used to save data into a file?

• SAVE
• LOAD
• STORE
• DUMP

Question 7: In Pig, all tuples in a relation must have the same number of fields. True or false?

• True
• False

Question 8: Which Pig relational operator is used to select tuples from a relation based on some criteria?

• transform
• filter
• group
• order by

Question 9: Which Pig relational operator is used to combine all the tuples in a relation that have the same key?

• union
• transform
• filter
• group
• join

Question 10: Which Pig relational operator is used to combine two or more relations using one or more common field values?

• union
• transform
• filter
• group
• join

Question 11: The Pig Tokenize evaluation operator splits a string and outputs a bag of words. True or false?

• True
• False

Question 12: When using the Pig Count evaluation operator, you must also use either the Group All or the Group By operator. True or false?

• True
• False

Question 13: Which of the following Pig operators can be used to review the logical, physical, and MapReduce execution plans?

• Verbose
• Dump
• Store
• Explain

Question 14: Which of the following is a valid Pig evaluation operator?

• isempty
• count_star
• diff
• count
• All of the Above

Question 15: You can extend Pig via user defined functions. True or false?

• True
• False

Enroll Here: Text Analysis 101 Cognitive Class Exam Quiz Answers

Introduction to Text Analysis 101

Text analysis is the process of extracting meaningful information from written language. It involves various techniques to understand and interpret text, ranging from simple statistical methods to complex algorithms. The goal of text analysis is often to uncover patterns, sentiments, relationships, or other insights that can inform decision-making or further research.

There are several key components of text analysis:

1. Text Preprocessing: This step involves cleaning and preparing text data for analysis. It may include tasks such as removing punctuation, converting text to lowercase, removing stop words (common words like “and,” “the,” “is”), and stemming or lemmatizing words to reduce them to their base forms.
2. Tokenization: Breaking down text into smaller units, such as words or phrases (tokens), is essential for analysis. Tokenization can be straightforward for languages like English but more complex for languages without clear word boundaries.
3. Statistical Analysis: Basic statistical techniques like frequency analysis (counting how often specific words occur) and distribution analysis (examining the spread of words across a text or corpus) provide foundational insights into text content.
4. Sentiment Analysis: This technique aims to determine the sentiment or emotion expressed in a text. It can be done using lexicon-based approaches (matching words to sentiment dictionaries) or machine learning methods (training models to recognize sentiment).
5. Named Entity Recognition (NER): Identifying and classifying named entities (such as names of people, organizations, dates, etc.) in text is crucial for tasks like information retrieval and entity linking.
6. Topic Modeling: Discovering abstract topics within a collection of documents without prior labeling is achieved through topic modeling techniques like Latent Dirichlet Allocation (LDA). These methods help uncover themes or subjects that dominate a set of texts.
7. Text Classification: Assigning predefined categories or labels to text documents based on their content is useful for tasks like spam detection, sentiment classification, or topic categorization.
8. Natural Language Processing (NLP): NLP encompasses the broader field of computational techniques for understanding and manipulating human language. Text analysis is a subset of NLP focused specifically on extracting insights from text data.

Text analysis finds applications across various domains, including marketing (analyzing customer feedback), social media (tracking trends and sentiments), healthcare (extracting information from medical records), and many others where understanding textual data is critical.

In summary, text analysis leverages computational techniques to derive meaning and insights from textual data, enabling automated processing and understanding of large volumes of text efficiently.

Text Analysis 101 Cognitive Class Certification Answers

Module 1 – Getting to know IE Quiz Answers

Question 1: Your client would like to know how its advertising campaign impressed customers. Which IE task would provide this data?

• Event Extraction
• Co-reference resolution
• Sentiment Extraction
• Relation Extraction

Question 2: Which extraction phase can turn a dictionary match of a common first name plus an adjacent regular expression into a “potential person name” entity?

• None of these
• Entity Resolution
• Named Entity Recognition
• Feature Selection

Question 3: Consider a set of news articles that contains 20 mentions of person names. From this source, an extractor extracts 15 entities, 3 of which are incorrect. What are the Precision (P) and Recall (R) values?

• P = 0.30, R = 0.70
• P = 0.70, R = 0.30
• P = 0.80, R = 0.60
• P = 1.00, R = 1.30

Module 2 – Limitations in IE Quiz Answers

Question 1: Which of the following poses huge demands on the IE engine?

• Complex IE tasks
• Heterogeneous text inputs
• Different types of data
• All of the above

Question 2: A typical IE grammar-based workflow:

• Targets the step closest to the nature of the rule applied.
• Follows a unidirectional sequence of steps.
• Targets each step based on the nature of the input data.
• Creates a branched path according to the input and the desired output.

Question 3: We can overcome IE performance limitations by:

• Separating extractor semantics from execution strategy.
• Coupling extractor semantics with execution strategy.
• Parallel processing.
• Making faster finite state transducers.

Module 3 – Getting to know System T Quiz Answers

Question 1: What outputs do extractors generate in System T?

• Extractors
• Regular Expressions
• Annotations
• None of above

Question 2: An output refiner helps you to:

• Define multiple filters.
• Union multiple extractors.
• Define multiple extractors.
• None of above

Question 3: By selecting the Mapping Table checkbox in the Dictionary extractor, you can:

• Map dictionary terms against categories.
• Create a two-column dictionary.
• Add a column of metadata.
• All of the above.

Module 4 – IE with AQL Quiz Answers

Question 1: Which of the following statements describes AQL?

• AQL has a syntax that is similar to SQL.
• AQL has expressive power of algebra.
• AQL separates semantics from implementation.
• All of the above.

Question 2: What are the main advantages of SystemT’s approach towards Information Extraction?

• Richer and cleaner rule semantics
• Better performance through optimization
• Improved quality of results
• A and B

Question 3: Which of the following files can be part of an AQL module?

• Dictionary file
• AQL file
• UDF jar
• All of the above

Module 5 – AQL Basics Quiz Answers

Question 1: Which factor is essential for the Union All statement to work?

• The tuples should be from the same input text.
• The schemas of the tuples should be different.
• The schemas of the tuples should be from a single view.
• The schemas of the tuples should be same.

Question 2: Which of the following options is a valid consolidate policy?

• ContainsButNotEqual
• RightToLeft
• ExactEqual
• ContainedInside

Question 3: When is the Minus statement useful?

• When the two sets of input tuples have different schemas
• When you want to find matches for a sequence pattern
• When you want to subtract a set of tuples from another set of tuples
• All of the above

Module 6 – Advanced AQL Quiz Answers

Question 1: Which type of text can be extracted using the Detag statement?

• Semi-structured text
• Unstructured text
• Structured text
• None of above

Question 2: When should you use a standard tokenizer?

• When token boundaries are defined by punctuation and whitespace.
• When extraction of person names from Chinese text is needed.
• When extraction of parts of speech is required.
• All of the above.

Question 3: Which best practices should you use when developing an AQL module?

• Place large dictionaries and tables in separate modules.
• Avoid using the output view statement when developing extractor libraries.
• Document the source code using AQL Doc.
• All of the above.

Module 7 – Declarative IE and the System T optimizer Quiz Answers

Question 1: Which of the following leads to mistakes when two rules match the same region of text?

• Limited expressivity
• Lossy sequencing
• Rigid matching priority
• None of the above

Question 2: Which of the following strategies can overcome lossy sequencing?

• Expand rule patterns to include features such as aggregation.
• Impose modular tokenization.
• Include matching regimes that increase flexibility on priority.
• Use grammar rules that operate on graphs rather than sequences of annotations.

Question 3: In which stage of the SystemT optimizer do you merge block plans into a single operator graph?

• Post-processor
• Planner
• Pre-processor
• None of the above

Module 8 – Best Pratices Quiz Answers

Question 1: Why is it that the first document in a collection is often at the top of the AQL Profiler’s “hot” document’s view?

• The optimizer is trying to produce plans that are sensitive to each input document.
• This is because of how Java implements regex.
• This is due to the Java compiler.
• System T sorts documents by length for processing, so the first document is the longest.

Question 2: Which of the following is NOT a best practice for writing AQL?

• Use the AQL profiler to find and address hot spots.
• Follow simple rules of thumb when writing AQL.
• Don’t hand-tune while writing AQL.
• Always ignore throughput levels when designing extractors.

Question 3: Why is it necessary to be selective about performance tuning?

• It might adversely affect code readability
• It might reduce the quality of your results
• It might make your code more difficult to maintain
• A and B.
• A and C.

Text Analytics 101 Final Exam Answers

Question 1: Identify the logical sequence of phases in an IE system.

• Entity Identification > Feature Selection > Entity Resolution
• Entity Identification > Entity Resolution > Feature Selection
• Feature Selection > Entity Resolution > Entity Identification
• Feature Selection > Entity Identification > Entity Resolution

Question 2: Consider a set of news articles that contains 100 mentions of organizations. From this source, an extractor extracts 75 entities, 50 of which are correct. What are the Precision (P) and Recall (R) values of this extractor?

• P = 0.75, R = 0.50
• P = 0.67, R = 1.50
• P = 0.67, R = 0.50
• P = 0.50, R = 0.67

Question 3: What problem is caused by an IE system having a rigid matching priority?

• Regular expressions cannot be used when specifying rules.
• There is no support for matching strings spanning more than one token.
• The system cannot express aggregation operations.
• When multiple rules match the same region of text, mistakes are likely to occur.

Question 4: The System T consolidate policy:

• Applies a filtering predicate to output tuples.
• Specified how to handle tuples with overlapping spans.
• Specifies which tuple columns to group on.
• Specifies a tuple ordering.

Question 5: Which of the following AQL statements uses expressions, dictionaries, and sequence patterns to perform extraction?

• Relational style statement.
• Extract statement.
• Create table statement.
• Select statement.

Question 6: Which of the following statements are part of an AQL file?

• Create external table statements.
• Import statements.
• Create external dictionary statements.
• Export statements.
• All of the above.

Question 7: Which of the following types is a return value for table UDFs?

• Tuples.
• Integer.
• Span.
• Boolean.

Question 8: Which predicate would you use to check if a span is exactly equal to one of a predefined set of words?

• FollowsTok.
• MatchesRegex.
• MatchesDict.
• ContainsDict.

Question 9: Why is correct text tokenization important?

• Dictionary evaluation and many extraction operators, such as regex, are done on token boundaries, and incorrect tokenization will lead to incorrect results.
• Several built-in predicates and functions are token sensitive.
• AQL extract statements will not compile if tokenization is incorrect.
• A and B.
• A and C.

Question 10: Which of the following is NOT a best practice rule of thumb to follow when writing AQL?

• Use dictionaries instead of regex whenever possible.
• Make sure each module has its own copy of every dictionary.
• Avoid using UDFs as join predicates.
• Avoid Cartesian products.

Enroll Here: Data Science 101 Cognitive Class Exam Quiz Answers

Introduction to Data Science 101

Data Science is a multidisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It combines aspects of mathematics, statistics, computer science, and domain expertise to interpret data for decision-making purposes. The goal of data science is to uncover patterns, trends, and correlations that can guide strategic decisions, optimize processes, or solve complex problems.

Key components of data science include:

1. Data Collection: Gathering relevant data from various sources, which could be structured (e.g., databases) or unstructured (e.g., text documents, images).
2. Data Cleaning and Preprocessing: Ensuring data quality by handling missing values, removing duplicates, and transforming data into a suitable format for analysis.
3. Exploratory Data Analysis (EDA): Analyzing and visualizing data to summarize its main characteristics, often using statistical graphics and summary statistics.
4. Modeling and Algorithms: Applying mathematical models, machine learning algorithms, and statistical methods to analyze data and make predictions or decisions.
5. Interpretation and Communication: Interpreting the results of data analysis and communicating findings to stakeholders using data visualization, reports, or presentations.
6. Ethics and Privacy: Considering ethical implications related to data collection, usage, and privacy, ensuring responsible and unbiased data science practices.

Data science finds applications across various industries such as healthcare, finance, marketing, and e-commerce, where large volumes of data can be leveraged to gain competitive advantages, improve operations, and innovate.

In essence, data science empowers organizations to harness the power of data to drive informed decision-making and gain deeper insights into their operations and customers.

Data Science 101 Cognitive Class Certification Answers

Module 1 – Defining Data Science Quiz Answers

Question 1: From the reading In the report by the McKinsey Global Institute, by 2018, it is projected that there will be a shortage of people with deep analytical skills in the United States. What is the size of this shortage?

• 140 000 – 190 000 people
• 120 000
• 20 000 – 50 000 people
• 800 000 – 900 000 people
• 3 – 6 million people

Question 2: What has changed from the past to make Data science an in-demand occupation?

• There is now a lack of data
• Laws have changed
• Vast amount of data date being created
• The advent of the free market

Question 3: What is the minimum education requirement to become a data scientist?

• You must have a Degree in Computer Science
• You must have a Master’s degree in Statistics
• You must have a Ph.D. in Machine learning
• The above are all helpful, but they are not necessary to become a data scientist, education backgrounds of data scientists vary

Module 2 – What do data science people do Quiz Answers

Question 1: What is structured data??

• Data that can be stored in a database or some tabular form
• Images and video
• Segments of text
• Audio data

Question 2: What does the following formula represent: Base fair + Time x (Time in cab)

• The possible formula used in regression analysis to determine the cost of a cab ride
• The formula used to build a recommender system for rating a cab service
• A possible formula used in regression analysis used to determine the price of a house
• What is the impact of lot size on housing price?

Question 3: In the reading, what is an example of a question that can be put to a regression analysis?

• Do homes with brick exterior sell in rural areas?
• What is the impact of lot size on housing price?
• What are typical land taxes in a house sale?
• How much does a finished basement cost?
• How much should a house near a park cost?

Module 3 – Data Science in Business Quiz Answers

Question 1: Complete the following sentence that best explains why business needs to capture data: At the end of the day, for businesses, they know one thing, that if they are unable to measure something:

• they are unable to graph it
• they are unable to improve it
• they are unable to show compliance with tax laws
• they are unable to facilitate meetings between sales and marketing

Question 2: A business should never:

• delete data
• use Machen learning
• well document data
• use PowerPoint to deliver a message

Question 3: In the reading above, what is the role of the data scientist?

• Email the stakeholders about the analysis
• Manage a team of analysts to create a model
• Develop the strategy to fix the problems in the findings
• Use the insights to build the narrative to communicate the findings
• Use the data to tell the story the CEO wants to tell

Module 4 – Use Cases for Data Science Quiz Answers

Question 1: What popular product is primarily based on data science:

• Smartphone
• Google search
• Space X’s rockets
• Tesla’s Electric Cars

Question 2: From the readings the results section is where you present:

• The empirical findings
• R Squared
• The conclusion
• The contributors
• The methods used

Question 3: Complete the sentence: Predictions are useful?

• they are always correct
• but you need lots of data
• but they must come from a complicated model
• they are always wrong

Module 5 – Data Science People Quiz Answers

Question 1: In the reading, how does the author define ‘data science’?

• Data science is way of understanding things, of understanding the world
• Data science is a physical science like physics or chemistry
• Data science is some data and more science
• Data science is what data scientists do
• Data science is the art of uncovering the hidden secrets in data

Question 2: In the reading, what is admirable about Dr. Patil’s definition of a ‘data scientist’?

• His definition limits data science to activities involving machine learning
• His definition is only for people who program in Python
• His definition excludes statistics
• His definition is about weaving strong narratives into analytics
• His definition is inclusive of individuals from various academic backgrounds and training

Question 3: A good data scientist should?

• calculate confidence intervals
• be sceptical
• use complicated models
• only use big data

Data Science 101 Final Exam Answers

Question 1: In the reading, the output of a data mining exercise largely depends on:

• The engineer
• The programming language used
• The quality of the data
• The scope of the project
• The data scientist

Question 2: What has changed from the past to make Data science an in-demand occupation?

• There is now a lack of data
• Laws have changed
• Vast amount of data date being created
• The advent of the free market

Question 3: You develop an algorithm to predict rainy days, your algorithm predicts a rainy day, but the prediction is false. What is the following an example of?

• the r squared
• ture negatives
• false positive
• generated values

Question 4: What is an example of regression problem

• Finding an object in an image
• Reducing the size of a dataset
• Predicting the price of a house using the square footage
• Finding clusters in the data

Question 5: What should be a prime concern for storing data?

• Data safety and privacy
• Hiring the right database manager
• The size of the files
• The physical location of the servers
• Hadoop clusters

Question 6: What is a good starting point for data mining?

• Data Visualization
• Writing a data dictionary
• Non-parametric methods
• Creating a relational database
• Machine learning

Question 7: Complete the following sentence that best explains why business needs to capture data: At the end of the day, for businesses, they know one thing, that if they are unable to measure something:

• they are unable to graph it
• they are unable to improve it
• they are unable to show compliance with tax laws
• they are unable to facilitate meetings between sales and marketing

Question 8: When establishing data mining goals, the accuracy expected from the results also influences the:

• The timelines for the project
• The scope of the project
• The costs
• The presentation
• Data scientist

Question 9: When processing data, what factor can lead to errors in data?

• Synchronizing the database
• Changing services providers
• Renaming variables
• Human error
• Overfitting

Question 10: A good data scientist should?

• calculate confidence intervals
• be sceptical
• use complicated models
• only use big data