Certification

Enroll Here: NoSQL and DBaaS 101 Cognitive Class Exam Quiz Answers

Introduction to NoSQL and DBaaS 101

NoSQL Databases

NoSQL databases diverge from traditional relational databases (SQL databases like MySQL, PostgreSQL) in several key ways:

1. Flexible Data Models: NoSQL databases can handle a variety of data types and structures, making them more flexible than traditional relational databases which require a predefined schema.
2. Scalability: NoSQL databases are designed to scale horizontally, meaning they can efficiently distribute data across multiple servers. This scalability makes them suitable for handling large volumes of data and high throughput.
3. High Availability: Many NoSQL databases are designed with built-in replication and distribution mechanisms, ensuring high availability and fault tolerance.
4. Performance: Due to their architecture and data distribution methods, NoSQL databases often provide high performance for certain types of operations, such as read and write-intensive applications.
5. Types of NoSQL Databases:
   • Document Stores: Store data in flexible, JSON-like documents (e.g., MongoDB).
   • Key-Value Stores: Simplest NoSQL model where data is stored in key-value pairs (e.g., Redis, DynamoDB).
   • Column-family Stores: Organize data into columns instead of rows (e.g., Cassandra).
   • Graph Databases: Optimize for relationships between data elements (e.g., Neo4j).

Database as a Service (DBaaS)

DBaaS takes the management and maintenance of databases to the cloud, offering several advantages:

1. Managed Service: The cloud provider handles all aspects of database management, including setup, configuration, scaling, and backups.
2. Scalability: DBaaS platforms allow easy scaling of database resources up or down based on demand, often with just a few clicks or API calls.
3. Cost Efficiency: Pay-as-you-go pricing models mean organizations only pay for the resources they use, avoiding upfront infrastructure costs.
4. High Availability: Most DBaaS providers offer built-in redundancy and replication across multiple data centers, ensuring high availability and disaster recovery.
5. Security: DBaaS providers implement security best practices, such as encryption, access control, and compliance certifications, to protect data.

Integration and Usage

• Application Development: NoSQL databases are often favored for agile development environments where schema flexibility and scalability are critical.
• Big Data and IoT: NoSQL databases excel in handling unstructured and semi-structured data typical in big data and IoT applications.
• Cloud-Native Applications: DBaaS is particularly suited for cloud-native applications where rapid deployment and scalability are paramount.

In summary, NoSQL databases and DBaaS represent modern approaches to managing data that cater to the flexibility, scalability, and efficiency requirements of today’s applications and infrastructure landscapes.

NoSQL and DBaaS 101 Cognitive Class Certification Answers

Modules 1 – Introducing NoSQL Quiz Answers

Question 1: Scalability is a reason for employing a NoSQL database. True or false?

• True
• False

Question 2: Which of the following is a category of a NoSQL database?

• KeyValue
• Document
• Graph
• BigTable
• All of the above

Question 3: A NoSQL database is slightly more costly than a standard RDBMS. True or false?

• True
• False

Modules 2 – Defining NoSQL Database Types, Options, and Use Quiz Answers

Question 1: Which type of NoSQL database is the simplest?

• Graph
• BigTable/Column-Family
• Key-Value
• Document

Question 2: In a document database, documents have a flexible schema where more than one document can share the same information. True or false?

• True
• False

Question 3: Column-Family databases are worse than Graph databases when dealing with large amounts of sparse data. True or false?

• True
• False

Modules 3 – Choosing a Data Layer for Your Application Quiz Answers

Question 1: Since NoSQL databases offer horizontal scalability, they generally work well within a cloud architecture. True or false?

• True
• False

Question 2: Since many NoSQL databases primarily operate within a cluster, they cannot meet high availability requirements. True or false?

• True
• False

Question 3: Due to the flexibility and ease of integration of many NoSQL databases, it is often unnecessary to assess the skill sets of those who are developing applications. True or false?

• True
• False

Modules 4 – Introducing Cloudant – A NoSQLDBaaS Quiz Answers

Question 1: Cloudant scales very well to large amounts of

• Data and Concurrent Users
• Concurrent Users
• Data
• None of the above

Question 2: What file format does Cloudant use to store documents?

• TXT
• ASCII
• JSON
• XML

Question 3: Data can be replicated within clusters to keep them synchronized without administration intervention. True or false?

• True
• False

Modules 5 – Getting Started with IBM Cloudant:  A Hands-On Introduction Quiz Answers

Question 1: Database names should contain lowercase alphanumeric characters, and they are allowed to contain spaces. True or false?

• True
• False

Question 2: For each database, Cloudant creates a primary index and stores it in a T-Tree. True or false?

• True
• False

Question 3: Which of the following is a component of the Cloudant HTTP API hierarchy?

• Attachment
• Database
• Document
• Account
• All of the above

NoSQL and DBaaS 101 Final Exam Answers

Question 1: The term NoSQL refers to a family of databases that vary widely in style and technology. True or false?

• True
• False

Question 2: Which of the following is a characteristic of NoSQL databases?

• Fixed schemas are supported
• Datastores are non-relational
• The data model is always a set of tables
• All of the above

Question 3: What prompted the need for NoSQL databases?

• Companies could no longer afford to develop and host databases
• The dotcom boom in the late 90’s and early 2000’s
• Relational databases became obsolete
• All of the above

Question 4: Which of the following database solutions is NOT a NoSQL database?

• Oracle
• Mongo
• Apache CouchDB
• Riak

Question 5: In which of the following situations should you consider using a Key-Value NoSQL database?

• The database only needs to perform basic CREATE-READ-UPDATE-DELETE operations. The data to be housed is not highly related.
• The database will be housing sparse data sets or Big Data sets. There will be a need for compression or versioning control.
• The data is highly interconnected and business use-cases need to be able to trace relationships between data points.
• The database schema for insertions is not well-defined. The schema is likely to change over time and your solution needs to remain flexible.

Question 6: In which of the following situations should you consider using a Column-family NoSQL database?

• The database only needs to perform basic CREATE-READ-UPDATE-DELETE operations. The data to be housed is not highly related.
• The database will be housing sparse data sets or Big Data sets. There will be a need for compression or versioning control.
• The data is highly interconnected and business use-cases need to be able to trace relationships between data points.
• The database schema for insertions is not well defined. The schema is likely to change over time and your solution needs to remain flexible.

Question 7: In which of the following situations should you consider using a Document NoSQL database?

• The database only needs to perform basic CREATE-READ-UPDATE-DELETE operations. The data to be housed is not highly related.
• The database will be housing sparse data sets or Big Data sets. There will be a need for compression or versioning control.
• The data is highly interconnected and business use-cases need to be able to trace relationships between data points.
• The database schema for insertions is not well defined. The schema is likely to change over time and your solution needs to remain flexible.

Question 8: In which of the following situations should you consider using a Graph NoSQL database?

• The database only needs to perform basic CREATE-READ-UPDATE-DELETE operations. The data to be housed is not highly related.
• The database will be housing sparse data sets or Big Data sets. There will be a need for compression or versioning control.
• The data is highly interconnected and business use-cases need to be able to trace relationships between data points.
• The database schema for insertions is not well defined. The schema is likely to change over time and your solution needs to remain flexible.

Question 9: When using database-as-a-service, which aspect of development is left to the database team?

• Database Design
• Server Administration
• Server Software
• Hardware Maintenance

Question 10: If you require strong consistency and transactional rollback capabilities, which type of datastore will you most likely choose?

• Search Engine
• Document Database
• Relational Database
• Graph Database

Question 11: When using a hosted database solution, the provider is responsible for the underlying infrastructure, and your database administrators maintain the software and databases running on top of the infrastructure. True or false?

• True
• False

Question 12: Cloudant is based on which open-source NoSQL database technology?

• Riak
• Apache CouchDB
• DB2
• Apache HBase

Question 13: Cloudant stores all of your data on a single database server node. True or false?

• True
• False

Question 14: Cloudant’s replication can be effectively applied to which of the following customer use cases?

• Avoidance of vendor lock-in (e.g. replication to a local CouchDB instance)
• Client offline access for mobile devices or web browsers
• Hot backups for continuous replication to another cluster
• A data delivery network requiring continuous replication to geographically dispersed clusters
• All of the above

Question 15: Where can Cloudant Database-as-a-Service be deployed?

• Cloud environments based on virtual machines, such as Amazon Web Services
• Non-IBM bare metal cloud environments, such as Rackspace or Windows Azure
• IBM infrastructure, such as IBM SoftLayer
• All of the above

Question 16: What file format does Cloudant use to store data?

• Objects
• JSON documents
• Tables
• Columnar storage format

Question 17: Cloudant writes each document on three separate nodes. True or false?

• True
• False

Question 18: Which field can be used to look up a document in a Cloudant database by using the primary index?

• _rev
• _id
• primary
• key

Question 19: Which value types are supported by a Cloudant document?

• nested objects
• booleans
• arrays
• strings
• All of the above

Question 20: Which type of Cloudant index leverages Apache Lucene libraries?

• Geospatial Index
• Secondary Index with Map Reduce
• Search Index
• Primary Index

Question 21: Which type of deployment does Cloudant offer?

• Hybrid Cloud
• Private – Locally hosted
• Public – Database-as-a-service
• All of the above

Question 22: Which tools can you use to work with the HTTP API?

• POSTMan
• RESTClient
• cURL
• All of the above

Enroll Here: Machine Learning with Apache SystemML Cognitive Class Exam Quiz Answers

Introduction to Machine Learning with Apache SystemML

Apache SystemML is a powerful open-source platform designed to facilitate large-scale machine learning (ML) tasks. It aims to provide both flexibility and scalability, making it suitable for handling big data scenarios efficiently. Here’s an overview of Apache SystemML and its key features:

What is Apache SystemML?

Apache SystemML is a declarative machine learning platform that allows users to define their machine learning algorithms in a high-level language. It abstracts the underlying complexities of distributed computing and optimization, enabling data scientists and developers to focus more on the logic and structure of their algorithms rather than low-level implementation details.

Key Features

1. Declarative Language: SystemML uses a high-level, declarative language called DML (Declarative Machine Learning) for expressing machine learning algorithms. This language is designed to be intuitive and close to mathematical notation, making it easier for data scientists to write and understand complex algorithms.
2. Scalability: One of the primary strengths of Apache SystemML is its ability to scale efficiently. It leverages Apache Hadoop and Apache Spark frameworks for distributed computing, allowing it to process large datasets across clusters of computers.
3. Automatic Optimization: SystemML includes an optimizer that automatically transforms high-level algorithm descriptions (expressed in DML) into distributed, optimized execution plans. This helps in achieving high performance while executing machine learning tasks on big data.
4. Flexibility: It supports a wide range of machine learning algorithms, including linear regression, logistic regression, matrix factorization, and more. Users can also extend the system by integrating custom algorithms written in DML.
5. Interoperability: Apache SystemML can be integrated with other Apache projects such as Apache Spark, allowing seamless interaction and leveraging the strengths of both platforms.

Getting Started with Apache SystemML

To begin working with Apache SystemML, follow these steps:

1. Installation: Apache SystemML typically runs on Apache Spark. Install Apache Spark first and then configure SystemML to run on top of it.
2. Learn DML: Familiarize yourself with the DML language, which is used to specify machine learning algorithms in SystemML. DML is similar to R or Python in syntax and is specifically designed for expressing matrix and tensor operations common in machine learning.
3. Write and Execute Scripts: Start writing DML scripts to define and execute machine learning workflows. Use the built-in algorithms or implement custom ones as per your requirements.
4. Scale Up: As your data and computation needs grow, scale up your SystemML deployment by adding more nodes to your Apache Spark cluster.

Conclusion

Apache SystemML is a versatile tool for data scientists and engineers working with large-scale machine learning tasks. By providing a high-level language and leveraging distributed computing frameworks like Apache Spark, it simplifies the development and execution of complex machine learning algorithms on big data. Whether you are just starting with machine learning or managing advanced data science projects, Apache SystemML offers the scalability and flexibility necessary to tackle real-world challenges effectively.

Machine Learning with Apache SystemML Cognitive Class Certification Answers

Module 1 – What is SystemML Quiz Answers

Question 1: In machine learning, as analytical models are exposed to new data, they are able to independently adapt. True or false?

• True
• False

Question 2: Which of the following are types of alternatives to SystemML?

• R
• MLlib
• Spark R
• Mahout
• All of the above

Question 3: The R language was designed for machine learning and works great for big data. True or false?

• True
• False

Module 2 – SystemML and the Spark MLContext Quiz Answers

Question 1: What the ways you can use SystemML’s Spark MLContext?

• spark-shell
• Through an application using the API
• Through the SystemML console
• A notebook interface
• None of the above

Question 2: You must pass in the reference of the SparkContext to the MLContext constructor. True or false?

• True
• False

Question 3: Why would you use the Spark MLContext?

• Programmatic interface into SystemML’s libraries
• To benefit from the optimizations that come with SystemML
• When you need to convert the data to a binary block matrix
• A and B only
• None of the above

Module 3 – SystemML Algorithms Quiz Answers

Question 1: The Regression algorithm is an ensemble learning method that creates a model composed of a set of tree models for classification. True or false?

• True
• False

Question 2: K-means is an unsupervised learning algorithm used to assign a category label to each record so that each similar record tend to get the same label. True or false.

• True
• False

Question 3: The Kaplan-Meier algorithm predicts how likely it is someone will purchase a product of similar category. True or false?

• True
• False

Module 4 – Declarative Machine Learning (DML) Quiz Answers

Question 1: What does DML stand for?

• Data manipulation language
• Data machine language
• Declarative machine learning
• Declarative machine language

Question 2: To run a DML script, which of the following jar file is required at runtime?

• MLContext.jar
• DML.jar
• SystemML.jar
• spark-context.jar

Question 3: Which of the following way to pass command-line arguments is recommended?

• positional arguments
• named arguments
• a comma separated list
• a file

Module 5 – SystemML Architecture and Optimization Quiz Answers

Question 1: In the ALS performance comparison, at which dataset does the MLlib code run out of memory??

• Large
• Medium
• Small
• None

Question 2: Which of the following does NOT belong to the SystemML Optimizer stack?

• Create the RDDs for the high-level algorithm
• Compute memory estimates
• Generate runtime program
• Live variable analysis

Question 3: How does SystemML know it is better to run the code on one machine?

• Advanced Rewrites
• Propagation of statistics
• Live variable analysis
• Efficient runtime
• The developer tells it to

Machine Learning with Apache SystemML Final Exam Answers

Question 1: What is machine learning?

• Artificial intelligence for machines to make decisions
• Same as data science to gather insight using machines
• Enable computers to learn without being explicitly programmed
• Learning about how machines operate

Question 2: What is the purpose of SystemML?

• Programming language for big data
• In-memory analytics engine
• Machine learning for spark
• Machine learning on hadoop
• All of the above

Question 3: What are the challenges of machine learning on big data using R?

• Programmers are needed to convert the high-level code to low level code for parallel computing
• Each iteration of the code takes time to be rewritten and recompile
• Chances for errors are higher during the translation of the algorithms
• All of the above

Question 4: What is the vision of SystemML?

• Run the same algorithm developed for small data on big data
• Provide flexible algorithm of ML algorithms
• Automatic generation of hybrid runtime plans
• All of the above

Question 5: Which of the following languages is SystemML most similar?

• R
• Python
• Java
• Scala
• Perl
• R and Python
• Java and Scala

Question 6: Which of the following line of code will launch the Spark shell with SystemML?

• ./bin/spark-shell –jars SystemML.jar
• ./bin/spark-shell –executor-memory 4G –jars SystemML.jar
• ./bin/spark-shell –driver-memory 4G –jars SystemML.jar
• ./bin/spark-shell –executor-memory 4G –driver-memory 4G –jars SystemML.jar
• All of the above

Question 7: Why would you convert a DataFrame to a binary-block matrix?

• To enable parallelization within the Spark engine
• To use the rich set of APIs provided by the binary-block matrix
• Allows algorithm performance to be measured separately from data conversion time
• Allows a more efficient runtime processing of the data

Question 8: Which of the following is TRUE with regards to helper methods in SystemML?

• SystemML’s output is encapsulated in the MLContext object
• SystemML’s output is encapsulated in the MLOutput object
• Helper methods retrieves the values from the MLOutput object
• Helper methods retrieves the values from the MLContext object
• A and D only
• B and C only

Question 9: Which is NOT a benefit of using SystemML algorithms?

• Run in parallel
• It is faster than all other algorithms
• No need for translation into a lower-level language
• Algorithms are optimized based on data and cluster characteristics

Question 10: Which of the following classes of algorithms provide a recommendation?

• Regression
• Classification
• Matrix Factorization
• Descriptive statistics

Question 11: Which of the following algorithm can group a set of data into known categories?

• Regression
• Clustering
• Survival Analysis
• Classification

Question 12: Which of the following algorithm can be used for prediction, forecasting, or error reduction?

• Clustering
• Regression
• Survival Analysis
• Descriptive statistics

Question 13: Which of the following value typesis NOT supported in the DML language?

• String
• Double
• Varchar
• Boolean

Question 14: Matrix-vector operations avoids the need for creating replicated matrix for a certain subset of operations. True or false?

• True
• False

Question 15: Global variables cannot be access within a function. True or false?

• True
• False

Question 16: Which of the following are NOT types of categories of built-in functions in DML?

• Derivative built-in functions
• Matrix built-in functions
• Statistical built-in functions
• Casting built-in functions

Question 17: In the statistics propagation phase of the SystemML optimizer, what exactly is happening?

• To determine the confidence level of the computed results
• All the statistics is propagated to the top node to determine the most efficient runtime for query execution
• To determine of probability of the operation succeeding within a given period of time
• Find the widest matrix required and determine if it all fits into the heap.

Question 18: What is the benefit of doing the matrix rewrite?

• Reduce the line of code needed to represent the matrix
• To determine the confidence level of the computed results
• Clean up and unused memory from the matrix
• To enable parallelization of the given matrixithin a given period of time
• Represent the final matrix without computing the intermediate matrices

Question 19: Which is NOT part of the SystemML runtime for Spark?

• Automates critical performance decisions
• Distributed vs. local runtime
• Efficient linear algebra optimizations
• Automated RDD caching
• None of the above

Question 20: SystemML is an Apache open-source project. True or false

• True
• False

Enroll Here: Serverless Computing using Cloud Functions – Developer I Cognitive Class Exam Quiz Answers

Introduction to Serverless Computing using Cloud Functions – Developer I

Introduction to Serverless Computing

Serverless computing is a cloud computing execution model where cloud providers dynamically manage the allocation of machine resources. This means developers can focus on writing code and deploying functionalities without worrying about managing servers, scaling applications, or provisioning infrastructure.

Cloud Functions

Cloud Functions are a type of serverless compute service offered by cloud providers such as Google Cloud Platform (GCP) with Cloud Functions, Amazon Web Services (AWS) with Lambda, and Microsoft Azure with Azure Functions. These functions are event-driven, meaning they are triggered by events such as HTTP requests, database changes, file uploads, or scheduled tasks.

Key Concepts

1. Event-driven: Functions are executed in response to events. For example, an HTTP request to an endpoint can trigger a function to process and respond to the request.
2. Scalability: Cloud Functions automatically scale based on the number of incoming events. You don’t need to manage scaling configurations manually.
3. Pay-per-use pricing: You are charged based on the number of executions and the resources consumed during execution time, rather than paying for idle resources.
4. Statelessness: Functions are stateless by design. They are executed independently and do not maintain state between executions. State can be managed externally, typically using databases or external storage services.

Benefits

• Reduced operational overhead: Developers can focus more on writing code and less on managing infrastructure.
• Scalability: Functions automatically scale based on demand, from a few requests per day to thousands per second.
• Cost-efficiency: Pay only for the resources consumed during function execution, with no charges when functions are idle.

Use Cases

• APIs and microservices: Implement backend services without managing servers.
• Event processing: React to events such as file uploads, database changes, or IoT events.
• Scheduled tasks: Run code on a schedule, like cron jobs.

Getting Started

To get started with Cloud Functions:

1. Choose a provider: Decide on a cloud provider that offers serverless computing services (e.g., GCP, AWS, Azure).
2. Define your functions: Write the functions that will handle specific events or triggers.
3. Deploy and test: Deploy your functions to the cloud and test them with sample events.
4. Monitor and optimize: Monitor function performance and optimize as needed for cost and efficiency.

Conclusion

Serverless computing with cloud functions offers developers a scalable, cost-effective way to deploy code without managing infrastructure. By focusing on writing code and defining triggers, developers can build powerful applications with reduced operational overhead.

Serverless Computing using Cloud Functions – Developer I Cognitive Class Certification Answers

Module 1 – Serverless Essentials Quiz Answers

Question 1: What characteristics of serverless computing contribute to its name?

• Developers can run backend code to access databases without using servers.
• Serverless computing scales inherently to incoming events.
• Serverless computing does not use servers.
• Server management decisions are hidden from the developer.
• Serverless developers only need to configure the service framework for their chosen language.

Question 2: Serverless computing is a specific technology with a developed set of standards and practices.

• False
• True

Question 3: Serverless computing requires the developer to allocate servers and machine resources.

• False
• True

Question 4: Serverless computing scales inherently and executes stateless code in response to events.

• False
• True

Question 5: Isabelle decided to use serverless computing for a project that crops photos automatically when added to cloud storage. How would you critique her choice?

• Serverless is a great choice because it can flexibly react to incoming photos and crop them as needed.
• Serverless won’t work well here because it depends too much on how often customers will be uploading photos.
• Serverless may work well, but Isabelle will have to spend some time configuring servers to handle the infrequent uploads.
• Serverless is a great choice here because it allows Isabelle to manage the allocation of machine resources.

Question 6: You need to develop a project that is as cost-effective as possible. For which reasons would you pick serverless?

• Serverless outsourcing leads to less infrastructure, operational, and development costs.
• Serverless only charges for the time when code is executing.
• Serverless applications can run in low-cost, pre-allocated capacity you can purchase in advance.

Question 7: Long running and non-separable tasks are types of tasks that typically work well in a serverless environment.

• False
• True

Question 8: You are given the following graph of usage for an application you are developing. Which of the following is a correct conclusion of the graph?

• The usage is mostly sporadic, so serverless computing is a bad fit for this usage case.
• The usage is mostly sporadic, so serverless computing is a good fit for this usage case.
• The usage is mostly constant, so serverless computing would be a bad fit for this usage case.
• The usage is mostly constant, so serverless computing would be a good fit for this usage case.

Module 2 – Create and Invoke Action Quiz Answers

Question 1: If you were to execute the following command which invokes a Hello World action:

ibmcloud fn action invoke hello

it would return with an activation ID and an HTTP response generated by the function.

• False
• True

Question 2: When invoking actions, parameter values can be any valid JSON value, including nested objects.

• False
• True

Question 3: It is not possible to monitor activation logs in real time using the command line.

• False
• True

Question 4: It is impossible to perform a blocking invocation on an asynchronous action.

• False
• True

Question 5: If an action in the middle of a sequence fails, the platform will return the error after the last action in the sequence is invoked.

• False
• True

Module 3 – Manage Actions with Packages Quiz Answers

Question 1: You can invoke actions in public packages as if they were normal actions.

• False
• True

Question 2: You can provide default parameters to an action in a public package just as you would a normal action.

• False
• True

Question 3: If you bind a parameter value to a package, that value cannot be overridden on invocation.

• False
• True

Question 4: After creating a custom package as private, you can update the package to make it public.

• False
• True

Module 4 – Connect Actions to Event Sources Quiz Answers

Question 1: What is a trigger?

• An API that invokes a function
• A network connection between an event source and a function
• A named channel for a class of events
• Event data that is sent to functions as key-value pairs

Question 2: What is the purpose of a rule?

• associates multiple triggers with a single action
• associates a single trigger with a single action
• associates a single trigger with multiple actions

Question 3: Rules allow you to define conditional logic that controls if an action gets invoked based upon event data.

• True
• False

Question 4: Multiple actions can be fired from a single trigger.

• Correct
• Incorrect

Module 5 – Expose Actions as APIs Quiz Answers

Question 1: Any action can be made accessible on the web by simply setting the flag –web to true.

• False
• True

Question 2: Functions that are web actions need to parse the raw HTTP request header to obtain query parameters.

• False
• True

Question 3: Which command would you use to retrieve the HTTP endpoint for a web action?

• ibmcloud fn api get <action_name> –url
• ibmcloud fn action get <action_name> –url
• ibmcloud fn api get <action_name> –http
• ibmcloud fn action get <action_name> –http

Question 4: If you have a web action that returns a JSON object, the caller must append .json to the web action’s URL to get a successful response.

• False
• True

Module 6 – Use the Web User Interface (web UI) Quiz Answers

Question 1: When using the ICF Web UI, you can accomplish almost everything you can using the CLI.

• False
• True

Question 2: The ICF Web UI provides access to logs and action metrics.

• False
• True

Serverless Computing using Cloud Functions – Developer I Final Exam Answers

Question 1: ICF scales your actions automatically only in response to event triggers.

• False
• True

Question 2: You are given the following graph of usage for an application you are developing. Which of the following is a correct conclusion of the graph?

• The usage is mostly sporadic, so serverless computing is a bad fit for this usage case.
• The usage is mostly sporadic, so serverless computing is a good fit for this usage case.
• The usage is mostly constant, so serverless computing would be a bad fit for this usage case.
• The usage is mostly constant, so serverless computing would be a good fit for this usage case.

Question 3: Tobias is trying to create an image recognition model for object analysis using large amounts of raw image data. Is serverless a good choice in helping with these tasks?

• Serverless may be a good choice in both dividing the data into manageable pieces as well as analyzing the data in parallel.
• Serverless won’t work well here because processing the large amount of raw data would be too costly.
• Serverless is not a good choice because Tobias would need dedicated servers that have the processing power for analytics.
• Serverless is a great choice here, but Tobias will have to configure his account to handle the amount of processing needed.

Question 4: Actions cannot directly invoke other actions without using a sequence.

• False
• True

Question 5: You are able to pass parameters when invoking an action using the command line directly or indirectly using a parameter file.

• False
• True

Question 6: If you have many packages of actions, you can better organize them by creating a package that contains several similar packages.

• False
• True

Question 7: Multiple triggers can be configured to fire the same action.

• Correct
• Incorrect

Question 8: It may be possible to construct a website entirely using serverless actions.

• False
• True

Question 9: If you wish to monitor your action invocations, you need to use the Web UI.

• False
• True

Enroll Here: Build Swift Mobile Apps with Watson AI Services Cognitive Class Exam Quiz Answers

Introduction to Build Swift Mobile Apps with Watson AI Services

Building Swift mobile apps with Watson AI services opens up a world of possibilities for integrating powerful cognitive computing capabilities into your iOS applications. By leveraging Watson AI services, developers can enhance their apps with functionalities like natural language processing, image recognition, sentiment analysis, and more, all tailored to meet specific business needs or user requirements.

In this introduction, we’ll explore how you can get started with integrating Watson AI services into your Swift mobile apps. Here’s an outline to guide us:

1. Understanding Watson AI Services

• Overview: What are Watson AI services? How do they enhance mobile app capabilities?
• Types of Services: Highlight key services like Natural Language Understanding, Visual Recognition, Tone Analyzer, etc.
• Use Cases: Examples of how these services can be applied in real-world scenarios (e.g., customer support, healthcare, education).

2. Setting Up Your Development Environment

• Tools and Requirements: What tools do you need? (Xcode, IBM Cloud account, Watson SDKs).
• Creating IBM Cloud Services: Step-by-step guide to setting up Watson services via IBM Cloud.

3. Integrating Watson AI Services into Swift Apps

• Installing SDKs: How to integrate Watson SDKs into your Xcode project.
• Authentication: Setting up API keys and authentication for secure communication with Watson services.
• API Usage: Examples of API calls for different Watson services (e.g., analyzing text, recognizing images).

4. Building a Simple Swift App with Watson AI

• Sample Project: Walkthrough of a basic iOS app that utilizes Watson AI services.
• Code Examples: Highlight key snippets demonstrating integration and usage of Watson SDKs.

5. Best Practices and Considerations

• Performance: Optimizing app performance when using Watson AI services.
• Security: Ensuring secure data transmission and storage.
• Scalability: Design considerations for scaling AI capabilities as your app grows.

6. Next Steps

• Further Learning: Resources to deepen your understanding of Watson AI and Swift app development.
• Advanced Features: Explore more advanced features and services offered by Watson AI.
• Community and Support: Where to find help and community support for integrating Watson AI into your apps.

By the end of this introduction, you should have a foundational understanding of how to integrate Watson AI services into Swift mobile apps and be equipped to start experimenting with these powerful tools in your own projects.

Build Swift Mobile Apps with Watson AI Services Cognitive Class Certification Answers

Lab 1: Analyze Sentiment Quiz Answers

Question 1: You can use a Watson AI service to indicate whether a statement such as “This car is bad” is positive, negative, or neutral.

• True
• False

Question 2: The score that demonstrates that a sentence’s sentiment is recognized by the machine learning network with a confidence between 0 and +/- 1 means that there is a 0 – 100% confidence that the sentence is either negative or positive.

• True
• False

Question 3: In addition to sentiment, the Natural Language Understanding service analyzes emotions conveyed by specific target phrases or by the document as a whole. The emotion analysis for automatically detected entities and keywords can be enabled in this service.

• True
• False

Lab 2: Recognize Images Quiz Answers

Question 1: The Watson Visual Recognition service can identify which of the following types of content? Select all that apply.

• Foods
• Chemicals
• Animals
• Landscapes shown in running videos
• Text on signs

Question 2: You can expand the existing generic image classification model with new classifiers.

• True
• False

Question 3: The Visual Recognition service can process which of the following types of media to classify images and return a confidence score for relevant classifiers representing things such as objects, events, and settings. Select all that apply.

• Image URL
• Image file
• Film or movie URL 
• Film or movie file
• Audio file

Lab 3: Convert Text to Speech Quiz Answers

Question 1: The Watson Text to Speech service is authenticated with your Cloud (Bluemix) user ID and password.

• True
• False

Question 2: Which types of interfaces does the Text to Speech service offer? Select all that apply.

• FTP
• HTTP REST
• XML
• WebSocket

Question 3: The Text to Speech service understands text and natural language to generate synthesized audio output with the appropriate cadence and intonation.

• True
• False

Build Swift Mobile Apps with Watson AI Services Final Exam Answers

Question 1: You can train the default Visual Recognition Service by creating a temporary trial classifier to recognize certain types of images.

• True
• False

Question 2: You can access IBM Watson AI services only through REST APIs.

• True
• False

Question 3: The Watson Visual Recognition service can consume images as URL links.

• True
• False

Question 4: Which SDK must you use to call IBM Watson services from iOS applications?

• Java SDK
• .NET Framework SDK
• Watson Cloud Developer SDK
• Analytics SDK

Question 5: English is the only language that is supported by the Watson Text to Speech service.

• True
• False

Enroll Here: Mathematical Optimization for Business Problems Cognitive Class Exam Quiz Answers

Introduction to Mathematical Optimization for Business Problems

Mathematical optimization, or mathematical programming, involves selecting the best solution from a set of feasible alternatives. It uses mathematical models to describe the problem and finds the optimal solution by systematically evaluating possible solutions.

Types of Optimization Problems:

1. Linear Programming (LP):
   • Involves optimizing a linear objective function subject to linear equality and inequality constraints.
   • Example: Resource allocation, production planning.
2. Integer Programming (IP):
   • Extends linear programming by adding constraints that require some or all variables to be integers (0, 1, 2,…).
   • Example: Capital budgeting, job scheduling.
3. Mixed-Integer Programming (MIP):
   • Combines discrete (integer) and continuous variables in optimization problems.
   • Example: Facility location, portfolio optimization.
4. Nonlinear Programming (NLP):
   • Deals with optimizing objectives and constraints that are nonlinear functions.
   • Example: Supply chain optimization with nonlinear costs or demands.
5. Convex Optimization:
   • Focuses on optimizing convex functions over convex sets.
   • Example: Portfolio selection, risk management.

Steps in Mathematical Optimization:

1. Formulating the Problem:
   • Define the decision variables, objective function (what to maximize or minimize), and constraints (limitations or requirements).
2. Choosing the Optimization Method:
   • Select an appropriate optimization technique based on the problem structure (LP, IP, NLP, etc.).
3. Implementing the Model:
   • Use specialized software (e.g., MATLAB, Python libraries like scipy.optimize, commercial solvers like CPLEX or Gurobi) to set up and solve the optimization model.
4. Interpreting Results:
   • Analyze the optimal solution to make informed business decisions.

Applications in Business:

• Supply Chain Management: Optimize inventory levels, distribution routes.
• Finance: Portfolio optimization, risk management.
• Marketing: Resource allocation in advertising campaigns.
• Operations: Production planning, scheduling.
• Logistics: Vehicle routing, facility location.

Challenges:

• Complexity: Large-scale problems can be computationally intensive.
• Data Quality: Optimization models rely on accurate input data.
• Modeling Assumptions: Simplifications may affect real-world applicability.

Conclusion:

Mathematical optimization provides powerful tools for businesses to improve efficiency, reduce costs, and make data-driven decisions. By understanding the problem structure and leveraging appropriate techniques, businesses can gain competitive advantages in various domains.

Mathematical Optimization for Business Problems Cognitive Class Certification Answers

Module 1 – The Big Picture Quiz Answers

Question 1: True or false? Constraint Programming is particularly useful for solving scheduling problems and certain combinatorial optimization problems.

• False
• True

Question 2: True or false? A feasible solution can be, but is not guaranteed to be, an optimal solution.

• False
• True

Question 3: True or false? Objective functions always start with the words “maximize” or “minimize”.

• False
• True

Module 2 – Linear Programming Quiz Answers

Question 1: True or false? The following constraint is valid for a linear programming problem, where x and y are variables and z is a data item: 2x + 3y less than or equal to z²

• False
• True

Question 2: True or false? Hard constraints can be converted to soft constraints to help resolve infeasibilities.

• False
• True

Question 3: True or false? If a constraint is non-binding, its dual price will be zero.

• False
• True

Module 3 – Network Models Quiz Answers

Question 1: True or false? In a transportation problem, if all the capacities and demands are integer, then you can declare the variables to be continuous even though they are integer.

• False
• True

Question 2: True or false? The critical path is the shortest path in the network.

• False
• True

Question 3: True or false? A sequence of arcs connecting two nodes is called a path.

• False
• True

Module 4 – Beyond Simple LP Quiz Answers

Question 1: True or false? A piecewise linear function can be used to approximate convex nonlinear functions.

• False
• True

Question 2: True or false? The branch and bound method begins with LP relaxation.

• False
• True

Question 3: True or false? Mixed-integer programming is often used for investment planning.

• False
• True

Module 5 – Modelling Practice Quiz Answers

Question 1: True or false? Data sparsity can be exploited to create only the essential variables and constraints, thus reducing memory requirements.

• False
• True

Question 2: True or false? It is important to always use integer variables when a model involves the production of whole items.

• False
• True

Question 3: True or false? It is important to use penalties only when absolutely necessary.

• False
• True

Mathematical Optimization for Business Problems Final Exam Answers

Question 1: What are the two types of objects used to describe network structures?

• Nodes and arcs
• Arcs and chains
• Nodes and chains

Question 2: What are the possible reasons for an infeasible model?

• Real-world conflict
• Incorrect data
• Incorrect formulation
• All of the above

Question 3: True or false? Mathematical programming and constraint programming are the techniques you can apply using CPLEX.

• False
• True

Question 4: True or false? Binary variables are also known as Boolean variables.

• False
• True

Question 5: What is the first step of a typical optimization model development cycle?

• The identification of objectives, variables, and constraints
• The scope definition
• The creation of a prototype

Question 6: True or false? Basic variables take zero values in an iteration or final solution of the Simplex method.

• False
• True

Question 7: True or false? An unbounded variable always influences the solvability of a model.

• False
• True

Question 8: True or false? Flow conservation constraints are typically used in network models.

• False
• True

Question 9: True or false? Nonlinear terms and absolute values are not permitted in linear programming.

• False
• True

Question 10: True or false? Piecewise linear programming is used when dealing with functions consisting of several nonlinear segments.

• False
• True

Question 11: True or false? Very large linear programming models are often non-sparse.

• False
• True

Question 12: What does an optimization-based solution involve?

• An optimization engine
• Data
• An optimization model
• All of the above

Question 13: True or false? When all arcs in a chain are directed in such a way that it is possible to traverse the chain following the directions of arcs, it is called a path.

• False
• True

Question 14: A region is convex if …

• A straight line connecting two points inside the region passes outside it to get from one point to the other
• Any straight line between two points inside the region remains entirely in the region
• All of the above

Question 15: True or false? The scale of numbers used in an LP problem can affect computational time.

• False
• True

Enroll Here: Game-playing AI with Swift for TensorFlow (S4TF) Cognitive Class Exam Quiz Answers

Introduction to Game-playing AI with Swift for TensorFlow (S4TF)

Game-playing AI with Swift for TensorFlow (S4TF) can be a fascinating area to explore, combining the power of Swift’s expressive syntax with TensorFlow’s robust machine learning capabilities. Here’s an introduction to getting started with building game-playing AI using S4TF:

What is Swift for TensorFlow (S4TF)?

Swift for TensorFlow (S4TF) is a framework developed by Google that allows developers to leverage the Swift programming language for machine learning tasks, integrating seamlessly with TensorFlow. Swift is known for its safety, speed, and expressiveness, making it an excellent choice for developing AI applications.

Setting Up Swift for TensorFlow (S4TF)

Before diving into game-playing AI, you’ll need to set up S4TF on your machine:

1. Install Swift: Install Swift on your system. You can download pre-built toolchains from the Swift for TensorFlow GitHub repository or build from source.
2. Install TensorFlow: S4TF requires TensorFlow. Follow the instructions on the TensorFlow website to install TensorFlow for Swift.
3. Set Up IDE: Choose an IDE that supports Swift development. Xcode is a popular choice for macOS users, and there are options like VS Code with Swift support for other platforms.

Building a Game-Playing AI

Choose a Game

Select a game to build your AI for. Simple board games like Tic-Tac-Toe or Connect Four are good starting points due to their manageable complexity.

Designing the AI

Here’s a basic approach to designing a game-playing AI in Swift for TensorFlow:

1. Game Representation: Define how the game state is represented. This could involve creating a data structure or class to hold the board state and current player turn.
2. Game Logic: Implement the rules of the game, including valid moves and checking win conditions.
3. AI Model: Define a neural network or another model that will learn to play the game. S4TF allows you to define models using its API, leveraging TensorFlow’s operations.
4. Training the Model: Use reinforcement learning or supervised learning techniques to train your AI model. This involves feeding game states (input) and desired outcomes (output) into the model.
5. Evaluation: Evaluate your model’s performance against human players or other AI agents to refine its strategy.

By combining Swift’s clarity with TensorFlow’s power, Swift for TensorFlow opens up exciting possibilities for developing game-playing AI applications. Dive in, experiment, and enjoy building intelligent game agents with S4TF.

Game-playing AI with Swift for TensorFlow (S4TF) Cognitive Class Certification Answers

Module 1 – Set Up Your Environment Quiz Answers

Question 1: What kind of IBM Cloud account doesn’t require payment information and doesn’t expire?

• Trial
• Lite
• Pay-as-you-go

Question 2: Which of the following platforms does Swift NOT run natively on?

• Linux
• Darwin
• Windows
• None of the above

Module 2 – Understanding Swift’s Basic Quiz Answers

Question 1: Which of the following companies did Chris Lattner work at after Apple?

• IBM
• Microsoft
• Google
• Amazon
• Tesla

Question 2: For how many years was Swift in development before it was released to the public?

4 years

Question 3: What kind of programming language is Swift?

• Interpreted
• Compiled to machine code
• Compiled to byte code
• Transpiled

Question 4: Which HTTP server for Swift will this course use?

• Perfect
• Kitura
• Swifter
• Vapor

Module 3 – Tic Tac Toc (Minimax) Quiz Answers

Question 1: Why does our implementation of Minimax keep track of the depth of the tree?

• Stop at a certain depth
• Memoize game states
• Take into account the “straightforwardness” of moves

Question 2: How is the tic-tac-toe board stored?

• Row-major format
• Column-major format

Question 3: If minimax started from a blank board and had to calculate the best possible move, how many board states would it evaluate? For the sake of mathematical simplicity, assume the board must be filled completely to be considered “over” – players cannot win.

• 9!
• 9^2
• 9^9
• 9*9

Question 4: The “minimax” function returns the best move to take for a board state.

• True
• False

Module 4 – Cartpole (Reinforce Learning) Quiz Answers

Question 1: The cartpole game is what kind of problem?

Inverted Pendulum

Question 2: The @differentiable function decorator is an example of an implementation of what technology/technique?

Question 3: Why do we only train the network with the top 30% of episodes?

• To reduce training time with fewer samples
• To improve network performance with better samples

Question 4: OpenAI Gym…

• Provides easy-to-use game enviroments to test RL agents
• Ships with RL algorithms to use as agents

Note: Make sure you select all of the correct options—there may be more than one!

Module 5 – 2048 (Monte Carlo Tree Search) Quiz Answers

Question 1: The implementation of the 2048 game logic is made fast by:

• Using bitboards
• Pre-calculating moves and scores for rows
• Multi-threading

Note: Make sure you select all of the correct options—there may be more than one!

Question 2: Which framework did we use to enable Swift to host HTTP servers?

Kitura

Question 3: Monte Carlo Tree Search is…

• Stochastic
• Guaranteed to always give the correct answer

Note: Make sure you select all of the correct options—there may be more than one!

Question 4: Which swift file defines dependencies and other package details?

Package.swift

Game-playing AI with Swift for Tensor Flow (S4TF) Final Exam Answers

Question 1: Which tier(s) of IBM Cloud require payment information on file?

• Lite
• Trial
• Pay-as-you-go

Question 2: Why would you want to use a single language, Swift, over many languages, each specialized for a certain task?

• It’s easier to maintain a codebase written in a single language.
• Swift is an expressive, performant, and open-source language backed by a large company (Apple).
• It’s slow to facilitate communication between components in different languages.
• It reduces the amount of “reinventing the wheel” required across a codebase.

Question 3: Does Minimax plays perfectly all the time? If so, why?

• It’s trained on a lot of game data and learns how to play perfectly.
• It brute forces a solution based off of the rules of the game, and all possible future situations.
• It’s provided optimal heuristics.
• It doesn’t always play perfectly.

Question 4: Classes are passed by reference, and structs are always, indiscriminately passed by value.

• True
• False

Question 5: A computed property is…

• A variable within a struct/class/enum.
• A function within a struct/class/enum that’s accessed like a property.

Question 6: Why is Minimax penalized for looking at moves deeper into the game tree?

• To improve performance by looking at a more shallow game tree.
• So Minimax takes into account how straightforward a move is.
• To make Minimax more accurate.

Note: Make sure you select all of the correct options—there may be more than one!

Question 7: Why is Reinforcement Learning important?

• It’s more accurate than other training methods.
• It can learn by trial and error.
• It doesn’t require as much data to learn from.
• It can play games.

Note: Make sure you select all of the correct options—there may be more than one!

Question 8: Swift for TensorFlow is interoperable with Python, because Python’s written in C

• True
• False

Question 9: What are some reasons Swift for TensorFlow is special?

• Swift now has a wrapper around TensorFlow, enabling machine learning development.
• Swift for TensorFlow can automatically differentiate complex functions.
• Swift for TensorFlow can optimize complex tensor operations.

Note: Make sure you select all of the correct options—there may be more than one!

Question 10: For what reasons did we implement monte carlo tree search in a time-bounded manner?

• To reduce the amount of time the algorithm takes to search.
• To search game states more if they’re closer to the end of a game.
• To make MCTS more accurate.
• To distribute the algorithm across threads.

Enroll Here: Analyzing Big Data in R using Apache Spark Cognitive Class Exam Quiz Answers

Introduction to Analyzing Big Data in R using Apache Spark

Analyzing Big Data in R using Apache Spark combines the powerful data manipulation capabilities of R with the scalability and efficiency of Apache Spark’s distributed computing framework. This integration allows data scientists and analysts to work with large datasets that exceed the memory capacity of a single machine.

Key Concepts:

1. Apache Spark:
   • Apache Spark is an open-source distributed computing system that provides an interface for programming entire clusters with implicit data parallelism and fault tolerance.
   • It supports in-memory processing, which makes it much faster than traditional disk-based processing systems.
2. R and Spark Integration:
   • The sparklyr package in R provides an interface to Apache Spark from within the R environment.
   • sparklyr allows you to connect to a Spark cluster, manipulate data using familiar dplyr verbs, and perform advanced analytics.
3. Basic Workflow:
   • Connecting to Spark: Use spark_connect() to establish a connection to a Spark cluster.
   • Data Manipulation: Manipulate Spark DataFrames using dplyr functions (mutate(), filter(), group_by(), etc.) via sparklyr.
   • Machine Learning: Leverage Spark’s MLlib library for scalable machine learning tasks directly from R.

Steps to Get Started:

1. Set Up Apache Spark:
   • Install Apache Spark on your system or connect to a Spark cluster (local or remote).
2. Install Required Packages:
   • Install sparklyr package in R: install.packages("sparklyr").
   • Optionally, install dplyr for data manipulation: install.packages("dplyr").
3. Connecting to Spark:
   • Load sparklyr library: library(sparklyr).
   • Connect to Spark: sc <- spark_connect(master = "local") (replace "local" with your Spark master URL for a remote cluster).
4. Data Manipulation:
   • Load data into Spark: my_data_tbl <- spark_read_csv(sc, "path/to/data.csv").
   • Manipulate data using dplyr verbs: filtered_data <- my_data_tbl %>% filter(column_name > threshold).
5. Machine Learning with Spark MLlib (optional):
   • Train machine learning models: Use ml_* functions (e.g., ml_linear_regression(), ml_decision_tree()) from sparklyr.
6. Disconnecting from Spark:
   • When done, disconnect from Spark: spark_disconnect(sc).

Benefits:

• Scalability: Handle large datasets that exceed memory capacity.
• Speed: Utilize in-memory processing for faster computations.
• Integration: Leverage R’s extensive data manipulation and visualization capabilities with Spark’s distributed computing power.

Analyzing Big Data in R using Apache Spark thus empowers data scientists to tackle complex analyses and machine learning tasks that were previously impractical with traditional R-based approaches.

Analyzing Big Data in R using Apache Spark Cognitive Class Certification Answers

Module 1: Introduction to SparkR Quiz Answers

Question 1: What shells are available for running SparkR?

• Spark-shell
• SparkSQL shell
• SparkR shell
• RSpark shell
• None of the options is correct

Question 2: What is the entry point into SparkR?

• SRContext
• SparkContext
• RContext
• SQLContext

Question 3: When would you need to call sparkR.init?

• using the R shell
• using the SR-shell
• using the SparkR shell
• using the Spark-shell

Module 2: Data Manipulation in SparkR Quiz Answers

Question 1: dataframes make use of Spark RDDs

• False
• True

Question 2: You need read.df to create dataframes from data sources?

• True
• False

Question 3: What does the groupBy function output?

• An Aggregate Order object
• A Grouped Data object
• An Order By object
• A Group By object

Module 3: Machine Learning in SparkR Quiz Answers

Question 1: What is the goal of MLlib?

• Integration of machine learning into SparkSQL
• To make practical machine learning scalable and easy
• Visualization of Machine Learning in SparkR
• Provide a development workbench for machine learning
• All of the options are correct

Question 2: What would you use to create plots? check all that apply

• pandas
• Multiplot
• Ggplot2
• matplotlib
• all of the above are correct

Question 3: Spark MLlib is a module of Apache Spark

• False
• True

Analyzing Big Data in R using Apache Spark Final Exam Answers

Question 1: Which of these are NOT characteristics of Spark R?

• it supports distributed machine learning
• it provides a distributed data frame implementation
• is a cluster computing framework
• a light-weight front end to use Apache Spark from R
• None of the options is correct

Question 2: True or false? The client connection to the Spark execution environment is created by the shell for users using Spark:

• True
• False

Question 3: Which of the following are not features of Spark SQL?

• performs extra optimizations
• works with RDDs
• is a distributed SQL engine
• is a Spark module for structured data processing
• None of the options is correct

Question 4: True or false? Select returns a SparkR dataframe:

• False
• True

Question 5: SparkR defines the following aggregation functions:

• sumDistinct
• Sum
• count
• min
• All of the options are correct

Question 6: We can use SparkR sql function using the sqlContext as follows:

• head(sql(sqlContext, “SELECT * FROM cars WHERE cyl > 6”))
• SparkR:head(sql(sqlContext, “SELECT * FROM cars WHERE cyl > 6”))
• SparkR::head(sql(sqlContext, “SELECT * FROM cars WHERE cyl > 6”))
• SparkR(head(sql(sqlContext, “SELECT * FROM cars WHERE cyl > 6”)))
• None of the options is correct

Question 7: Which of the following are pipeline components?

• Transformers
• Estimators
• Pipeline
• Parameter
• All of the options are correct

Question 8: Which of the following is NOT one of the steps in implementing a GLM in SparkR:

• Evaluate the model
• Train the model
• Implement model
• Prepare and load data
• All of the options are correct

Question 9: True or false? Spark MLlib is a module SparkR to provide distributed machine learning algorithms.

• True
• False

Enroll Here: Kubernetes Operators Intermediate Cognitive Class Exam Quiz Answers

Introduction to Kubernetes Operators Intermediate

A Kubernetes Operator is a method of packaging, deploying, and managing a Kubernetes application. It extends the Kubernetes API to create, configure, and manage instances of complex stateful applications. Operators aim to automate the routine tasks that are typically performed by human operators, such as deploying an application, scaling it, and handling upgrades or backups.

Intermediate Concepts in Kubernetes Operators

1. Custom Resource Definitions (CRDs):
   • CRDs are the primary building blocks of Operators. They extend the Kubernetes API to create custom resources that represent your application or service.
   • Intermediate operators often involve more sophisticated CRDs that define complex configurations, lifecycle management, and operational behaviors specific to the application.
2. Controller Logic:
   • Operators are essentially controllers that watch resources in the Kubernetes cluster and act upon changes or events related to those resources.
   • Intermediate operators often have more advanced controller logic:
     • Implementing state machines to manage complex application lifecycles.
     • Handling reconciliation loops to ensure the desired state of resources.
     • Incorporating error handling and retry mechanisms for robustness.
3. Operator SDK:
   • The Operator SDK is a framework that helps developers build Kubernetes Operators more efficiently.
   • Intermediate operators may utilize advanced features of the Operator SDK:
     • Using the SDK’s Operator Lifecycle Manager (OLM) to manage the lifecycle of Operators themselves.
     • Integrating with existing Operator Framework components to streamline development and deployment workflows.
4. Operational Tasks:
   • Beyond basic deployment and scaling, intermediate operators tackle more advanced operational tasks:
     • Implementing day-2 operations such as backup and restore mechanisms.
     • Adding monitoring and alerting configurations to ensure high availability and reliability.
     • Incorporating security best practices like RBAC (Role-Based Access Control) and network policies.
5. Handling Upgrades and Rollbacks:
   • Upgrading and rolling back applications in a Kubernetes environment can be complex. Operators can automate these processes.
   • Intermediate operators often include:
     • Automated upgrade strategies with minimal downtime.
     • Rollback mechanisms that revert to a previous stable state in case of failures during upgrades.
6. Observability and Metrics:
   • Monitoring the health and performance of applications managed by Operators is crucial.
   • Intermediate operators integrate:
     • Prometheus metrics for monitoring application performance.
     • Implementing custom metrics to track application-specific KPIs.
     • Logging configurations to capture and analyze operational logs.
7. Integration with Ecosystem Tools:
   • Kubernetes Operators often need to integrate with other tools and services in the Kubernetes ecosystem.
   • Intermediate operators might involve:
     • Integrating with service meshes like Istio for advanced networking capabilities.
     • Leveraging Helm charts or operators for managing dependencies and complex application topologies.

Conclusion

Intermediate Kubernetes Operators go beyond basic deployment and scaling to tackle more sophisticated challenges in managing stateful applications. They leverage advanced Kubernetes features, robust controller logic, and integration with the broader ecosystem to automate complex operational tasks effectively. Mastering these concepts empowers developers to build resilient, scalable, and efficient Kubernetes-based applications.

Kubernetes Operators Intermediate Cognitive Class Certification Quiz Answers

Module 1 – Introduction to Operators Quiz Answers

Question 1: How does a user interact with an operator?

• By accessing the operator directly
• By using kubectl or whatever tool they use to interact with the normal Kubernetes API
• By using the operator-sdk CLI
• By using an operator-specific CLI

Question 2: What does the command “operator-sdk init” do?

• Initializes a new controller image for an operator
• Initializes the basic scaffolding for a brand new operator
• Initializes a new controller for an operator
• Initializes the a new Custom Resouce Definition for an operator

Question 3: There is one architecture pattern that all operators follow.

• True
• False

Question 4: How was the Memcached operator added to the cluster?

• By altering the core Kubernetes images to include our additional components
• By creating normal Kubernetes resources such as Deployments and Cluster Role Bindings
• By deploying components external to Kubernetes and connecting them to the cluster
• By modifying the behavior of core Kubernetes components

Question 5: What is the controller of our memcached operator responsible for?

• The reconciliation the desired state in the etcd store with the actual state running on the cluster
• Creating the new memcached type on our Kubernetes cluster
• Storing data about requested memcached objects
• Receiving and validation incoming API requests from users

Module 2 – Helm Operators Quiz Answers

Question 1: The Spec fields of our Custom Resource directly correlate to what part of the original Helm chart?

• Values.yaml
• The template files
• Chart.yaml

Question 2: Helm operators allow a fine degree of control over the reconciliation of your operator.

• True
• False

Question 3: What sets the suffix for any API groups the Helm operator will create or use?

• The range
• The domain
• The subset
• The group

Question 4: In the Kubernetes API, what is an API group?

• A collection of endpoints that contain related functionality.
• Any resource that consists of multiple dependent resources.
• An endpoint that contains related functionality.
• A group of related resources.

Question 5: The Helm operator controller we used was an off-the-shelf image that required no additional configuration.

• True
• False

Module 3 – Ansible Operators Quiz Answers

Question 1: What does an Ansible Role consist of?

• A collection of Kubernetes Custom Resources
• A collection of tasks and related scaffolding such as scripts, environment variable, etc. (correct)
• A collection of Ansible Playbooks
• A collection of scripts to run in order

Question 2: Why should you fill out the Custom Resource Definition in an Ansible operator?

• So that Kubernetes users will be able to use the Custom Resource
• So that Kubernetes users will be able to see what fields the Custom Resource should have
• So that Kubernetes admins will be able to allow access to the CRD
• So that the resulting Custom Resource will work

Question 3: The ansible-operator controller automatically populates the Kubernetes status of Custom Resource objects with the Ansible status of the Ansible object.

• True
• False

Question 4: What is the name of the file that configures the Ansible Role?

• ansible.yml
• main.yml
• crd.yml
• role.yml

Question 5: What of the following commands can be used to generate an Ansible Role?

• operator-sdk generate bundle
• operator-sdk create api
• operator-sdk create role
• operator-sdk init

Kubernetes Operators Intermediate Final Exam Answers

Question 1: Operators always consist of a Custom Resource Definition and a controller.

• True
• False

Question 2: When creating an operator with Operator-sdk, what does setting the domain do?

• Sets the
• Sets the suffix for any API groups the operator resources will exist in
• Sets the domain for any services created to access operator resources
• Sets the prefix for any API groups the operator resources will exist in

Question 3: In an Ansible operator, what is the actual source of truth for the behavior of our Memcached Custom Resource?

• The Custom Resource type itself
• The Ansible Role
• The Ansible Playbook
• The Custom Resource definition

Question 4: Why is creating new RBAC rules is required for an operator?

• To allow the Custom Resource access to the controller
• To allow the controller to see and modify our Custom Resource
• To allow the Custom Resource Definition access to the controller
• To allow the controller to see and modify our Custom Resource Definition

Question 5: Which of the following initializes the basic scaffolding for a brand new operator?

• operator-sdk start
• operator-sdk init
• operator-sdk generate bundle
• operator-sdk create api

Question 6: For a Golang operator, in types.go, MemcachedSpec and MemcachedStatus correspond directly with the Spec and Status of the resulting memcached Custom Resource.

• True
• False

Question 7: Which component of the Golang operator is responsible for the new memcached type?

• Controller
• Custom Resource Definition
• RBAC rules
• Custom Resource

Question 8: What does the following operator-sdk command do: “operator-sdk create api –kind Memcached –controller=true”

• Create the scaffolding for only the contoller for an operator
• Create the scaffolding for the Custom Resource Definition and controller of an operator
• Create the scaffolding for only the Custom Resource Definition for an operator
• Create the basic scaffolding for a new operator

Question 9: Golang operator controllers are based on a community image maintained by the operator-sdk community.

• True
• False

Question 10: How were the operators in the module added to the Kubernetes cluster?

• By altering the core Kubernetes images to include our additional components
• By creating normal Kubernetes resources such as Deployments and Cluster Role Bindings
• By deploying components external to Kubernetes and connecting them to the cluster
• By modifying the behavior of core Kubernetes components

Question 11: When is a Helm operator a good choice over other operator types?

• You have a preexisting Helm chart for your resource.
• You want to get something running as quickly as possible.
• You’re unfamiliar with reconciliation loops and want try writing an operator.
• All of the above.

Question 12: In the Ansible operator, what is the name of Ansible resource that is a collection tasks and related scaffolding such as scriptts and environment variables?

• Scenario
• Role
• Script
• Playbook

Question 13: A Kubernetes user can only interact with resources from operators with special tools designed specifically for that operator.

• True
• False

Question 14: How can an operator be deployed to a Kubernetes cluster?

• As a program running outside the Kubernetes cluster.
• As a program manually deployed inside the Kubernetes cluster using Deployments, Pods, etc.
• As a program deployed inside the Kubernetes cluster using Operator Lifecycle Manager
• All of the above

Question 15: To write a Helm operator, you must create a new Helm chart.

• True
• False

Enroll Here: Kubernetes Operators Advanced Cognitive Class Exam Quiz Answers

Introduction to Kubernetes Operators Advanced

Kubernetes Operators are a method of packaging, deploying, and managing applications in Kubernetes. They use custom resources to extend Kubernetes functionality and automate complex tasks. Operators encapsulate operational knowledge about specific applications, allowing Kubernetes to manage them more effectively.

Advanced Concepts in Kubernetes Operators

1. Custom Resources and Controllers:
   • Custom Resources: Operators define custom resources (CRs) specific to the application they manage. These resources describe the desired state of the application.
   • Controllers: Operators include controllers that watch for changes in custom resources and reconcile the actual state of the application with its desired state.
2. Operator SDK:
   • The Operator SDK is a framework that helps developers build Kubernetes Operators more easily. It provides tools and libraries to streamline the creation, testing, and packaging of Operators.
   • SDK versions include support for different languages (Go, Ansible, Helm).
3. Operator Lifecycle Manager (OLM):
   • OLM is a component of Kubernetes that helps manage the lifecycle of Operators. It facilitates installation, updates, and removal of Operators from a Kubernetes cluster.
   • OLM ensures that Operators are installed correctly and that they adhere to best practices for operation and security.
4. Operator Patterns:
   • Singleton Operator: Manages a single instance of an application.
   • Multi-instance Operator: Manages multiple instances of an application.
   • Day-2 Operations: Handles ongoing operations such as scaling, upgrades, backups, and monitoring.
   • Monitoring and Metrics: Operators can integrate with Kubernetes monitoring tools (Prometheus, Grafana) to provide metrics and alerts.
5. Operator Frameworks:
   • Helm Operator: Converts existing Helm charts into Operators, allowing for easier management and updates.
   • Ansible Operator: Uses Ansible playbooks to define application management tasks, making it accessible to those familiar with Ansible.
6. Advanced Use Cases:
   • Automated Scaling: Operators can implement auto-scaling based on metrics, adjusting resources as needed.
   • Disaster Recovery: Operators can facilitate disaster recovery processes, restoring applications to a known good state.
   • Stateful Applications: Managing stateful applications requires advanced Operator logic for data management, backups, and migrations.

Benefits of Kubernetes Operators

• Automation: Operators automate routine tasks and operational procedures, reducing manual intervention.
• Consistency: Operators enforce best practices and consistency across deployments.
• Scalability: They enable efficient scaling of applications based on workload demands.
• Extensibility: Operators are extensible and can be customized to fit specific application requirements.

Conclusion

Kubernetes Operators represent a significant advancement in managing Kubernetes-native applications. They encapsulate operational knowledge and automate complex tasks, making Kubernetes more powerful and easier to manage. As you delve deeper into Operators, you’ll discover more advanced patterns, frameworks, and use cases that enhance application management within Kubernetes environments.

Kubernetes Operators Advanced Cognitive Class Certification Answers

Module 1 – Reconciliation Loops Quiz Answers

Question 1: What are the results that can be returned from a single iteration of a reconcile loop?

• Requeue, Exit without requeuing
• Return and requeue, Return an error and requeue, Exit without requeuing
• Exit, Exit with error
• Return and requeue, Return an error, Exit without requeuing

Question 2: The reconciliation loop is where the actual behavior of an operator is defined.

• True
• False

Question 3: Why is it a good idea to build a reconciliation loop out of small, iterative steps?

• The desired state of the cluster can change during reconciliation
• The actual state of the cluster can change during reconciliation
• The reconciliation request might be picked up by multiple controllers
• All of the above

Question 4: What do the Kubebuilder markers we put above the Reconcile method declaration do?

• The reconciliation request might be picked up by multiple controllers
• Create the RBAC for the controller to access the specified resources
• Scaffold the custom resource types for the operator
• Scaffold the reconcile loops for the specified resource

Question 5: It is possible for the object you are reconciling to no longer exist by the time the request is being processed.

• True
• False

Module 2 – Operator Lifecycle Manager Quiz Answers

Question 1: What are the three main components of a bundle?

• Sample, Config, and Image
• Manifests, Metadata, and a Dockerfile
• Config, an Operator image, and a bundle image
• Data, Metadata, and a Dockerfile

Question 2: A conversion webhook is a special kind of admission webhook.

• True
• False

Question 3: Why is it important to include a conversion webhook when upgrading the API version of an operator?

• To convert running instances to the new version of Memcached
• To migrate requests from users and controllers to the new version
• To migrate the database records to the new version
• To convert pre-existing instances of the resource to the new version

Question 4: Kubernetes conversion logic is based on what kind of model?

• Many to Many
• Hub and Spoke
• Up and Down
• Wheel and Deal

Question 5: Although the underlying custom resources that OLM uses may change, the format of an operator bundle should stay the same moving forward.

• True
• False

Module 3 – Scorecard Quiz Answers

Question 1: In the config file, each Scorecard test consists of what three components?

• Operator image, Test image, Test command
• Image, Entrypoint, Labels
• Test file, Commands, Bundle
• Dockerfile, Entrypoint, Name

Question 2: Scorecard tests execute from your local machine targeting the cluster.

• True
• False

Question 3: Why did we have to cross compile our Scorecard test?

• To include our Scorecard config
• It’s executed in a container on the cluter, which is a 64-bit Linux environment
• To include our operator’s bundle
• To ensure all the dependencies were included

Question 4: Why did we run our Scorecard test with our operator’s Service Account?

• To give Scorecard the RBAC permissions to run the test container
• To give it the RBAC permissions needed to manipulate the Memcached and dependant types
• To give it the RBAC permissions to manipulate our operator’s controller
• To tell it which operator to test

Question 5: The Scorecard config is part of your operator’s bundle.

• True
• False

Kubernetes Operators Advanced Final Exam Answers

Question 1: Your Scorecard config is automatically generated as part of your operator’s bundle.

• True
• False

Question 2: Why is it important to include a conversion webhook when upgrading the API version of an operator?

• To convert running instances to the new version of Memcached
• To migrate requests from users and controllers to the new version
• To migrate the database records to the new version
• To convert pre-existing instances of the resource to the new version

Question 3: Your bundle starts with some automatically generated Scorecard tests that test what features of your operator?

• Regression tests to test upgrading your operator
• Basic best practices like descriptors and validators for your custom resource types
• Benchmarks to stress-test your operator
• Basic unit tests that test your custom resource types

Question 4: What are the results that can be returned from a single iteration of a reconcile loop?

• Requeue, Exit without requeuing
• Return and requeue, Return an error and requeue, Exit without requeuing
• Exit, Exit with error
• Return and requeue, Return an error, Exit without requeuing

Question 5: Conversion between different API versions of the same resource is based on a Hub-and-Spoke model.

• True
• False

Question 6: Why should you start a reconciliation loop by fetching the object you’re reconciling?

• To get the most recent API version of the object
• The object may have been deleted, updated, or otherwise invalidated in the meantime
• To fetch the object’s schema
• The object object isn’t included in the reconciliation request

Question 7: A conversion webhook is a special type of which kind of default Kubernetes resource?

• Controller webhook
• Mutating webhook
• Upgrade webhook
• Admission webhook

Question 8: Why is it a good idea to build a reconciliation loop out of small, iterative steps?

• The desired state of the cluster can change during reconciliation
• The actual state of the cluster can change during reconciliation
• The reconciliation request might be picked up by multiple controllers
• All of the above

Question 9: What are the three main components of a bundle?

• Sample, Config, and Image
• Manifests, Metadata, and a Dockerfile
• Config, an Operator image, and a bundle image
• Data, Metadata, and a Dockerfile

Question 10: A Scorecard test requires a Service Account with permissions to access your custom resource types.

• True
• False

Enroll Here: Building Robots with TJBot Cognitive Class Exam Quiz Answers

Introduction to Building Robots with TJBot

Building robots with TJBot is an exciting endeavor that combines hardware and software to create a customizable, interactive robot. TJBot, short for “Technology/Toy Joint Bot,” was originally created by IBM as an open-source project designed to integrate IBM Watson services with a Raspberry Pi and various peripherals.

Getting Started with TJBot

1. Hardware Requirements:
   • Raspberry Pi: Preferably a Raspberry Pi 3 or 4, although other models may also work.
   • SD Card: To host the operating system (e.g., Raspbian).
   • Microphone: For voice interaction capabilities.
   • Speaker: For audio output.
   • LED: To add visual feedback.
   • Servo Motor: Optional for additional movements.
2. Software Requirements:
   • Raspbian OS: Install the latest version on your Raspberry Pi.
   • Node.js: Required for running TJBot’s Node.js library.
   • TJBot library: Install the TJBot Node.js library from GitHub.
3. Setting Up TJBot:
   • Assemble Hardware: Connect the microphone, speaker, LED, and any additional components.
   • Configure Software: Set up Raspbian OS, install Node.js, and then install the TJBot library.
   • Connect to Watson Services: Create IBM Cloud services (like Watson Assistant for conversation, Watson Visual Recognition for image recognition, etc.) and obtain API keys.

Programming TJBot

• Node.js Programming: TJBot is primarily programmed using Node.js. You can write scripts to control TJBot’s movements, interact with its sensors, and utilize IBM Watson services for AI capabilities.
• Example Projects:
  • Voice Interaction: Use the microphone and speaker to create a voice-controlled assistant.
  • Image Recognition: Utilize the camera (if attached) and Watson Visual Recognition to identify objects.
  • Language Understanding: Integrate Watson Assistant to create a chatbot that responds to user queries.
  • Emotion Analysis: Analyze facial expressions using Watson services coupled with a camera.

Resources and Community

• Official Documentation: Refer to IBM’s official documentation and GitHub repository for detailed guides and examples.
• Community Support: Engage with the TJBot community through forums, GitHub discussions, and social media channels to share ideas and troubleshoot issues.

Conclusion

Building robots with TJBot offers a hands-on way to explore AI, robotics, and IoT integration. By combining TJBot’s hardware capabilities with IBM Watson’s AI services, you can create innovative projects ranging from simple voice assistants to complex interactive systems. Get started today by gathering the necessary hardware, setting up the software environment, and exploring the endless possibilities of TJBot!

Building Robots with TJBot Cognitive Class Certification Answers

Quick Labs Quiz Answers

Question 1: What does the see method return? Select all that apply.

• Names of objects
• Names of colors
• Names of people
• An image taken with the camera

Question 2: Which of these statements are true about TJBot? Select all that apply.

• TJBot is a product sold by IBM.
• TJBot is an open-source project.
• TJBot is powered by artificial intelligence.
• TJBot has a community of contributors who contribute recipes.

Question 3: What programming languages are supported by the TJBot libraries mentioned in this course? Select all that apply.

• Node.js
• Node-RED
• Swift
• Python

Question 4: Which one of these Watson services does not have a corresponding method in the TJBot library.

• Speech to Text
• Language Translator
• Discovery
• Text to Speech
• Visual Recognition
• Watson Assistant
• Tone Analyzer

Question 5: Which method can be passed a duration value to specify how long the LED light should stay on?

• shine
• blink
• pulse
• turnOn

Complex Labs Quiz Answers

Question 1: In which order do these methods described in the course make the most logical sense?

• isTranslatable, identifyLanguage, translate
• translate, identifyLanguage, isTranslatable
• identifyLanguage, isTranslatable, translate
• isTranslatable, translate, identifyLanguage

Question 2: var TJBot = require(“tjbot”);

var tj = new TJBot( 

  [“camera”], 

  {}, 

  {

     visual_recognition: {

       apikey: “”   

     }

  });

tj.see(function(response) { 

  console.log(response);

});

• Names of people
• An array of objects containing names of objects and colors seen by the TJBot camera
• An image captured by TJBot
• Faces of people

Question 3: Which number points in the following Node.js code (#1, #2, #3, #4) should be corrected so that TJBot can say “Hello World” in English? Select all that apply.

var TJBot = require(“tjbot”);

var tj = new TJBot( 

  [“microphone”],  // #1

  {

    robot: {

      gender: “female” // #2

    },

    speak: {

      language: “fr-FR” // #3

    }

  },

  {

    text_to_speech: { // #4

      username: “cf63b1f3-ef18-4628-86c8-6b1871e076b9”,        

      password: “MWNwz3qcdIab”

    }

  });

tj.speak(“Hello World”);

• #1
• #2
• #3
• #4

Question 4: Review the following Node.js code. Which number points (#1, #2, #3, #4) in the following code should be corrected to make TJBot take a picture and speak out what is seen? Select all that apply.

var TJBot = require(“tjbot”);

var tj = new TJBot( 

  [“speaker”],  // #1

  {

    robot: {

      gender: “female” // #2

    },

    speak: {

      language: “en-US” // #3

    }

  },

  { // #4

    text_to_speech: {

      username: “cf63b1f3-ef18-4628-86c8-6b1871e076b9”,        

      password: “MWNwz3qcdIab”

    }

  });

tj.see(function(response) {

  tj.speak(“I see “+response.map(i => i.class).join(“, “));

}); 

• #1
• #2
• #3
• #4

Question 5: Which of the following blocks will speak out the phrase “Hello World” followed by pulsing the LED light the color green?

CODE BLOCK 1:

tj.speak(“hello world”).then(function() {

  tj.pulse(“green”, 1);

});

CODE BLOCK 2:

tj.speak(“hello world”);

tj.pulse(“green”, 1);

CODE BLOCK 3:

tj.speak(“hello world”).then(function() {

tj.pulse(1, “green”);

});

CODE BLOCK 4:

tj.pulse(“green”, 1);

tj.speak(“hello world”);

• CODE BLOCK 1
• CODE BLOCK 2
• CODE BLOCK 3
• CODE BLOCK 4

Building Robots with TJBot Final Exam Answers

Question 1: What is the best way to determine whether a model is available in the Watson Language Translator service?

• Use the translate method
• Use the isTranslatable method
• Check the Watson Language Translator documentation

Question 2: The TJBot capabilities can be extended both physically and in code by using third-party libraries or by writing custom code.

• True
• False

Question 3: The converse method can be used to do which tasks? Select all that apply.

• Parse and process natural language
• Have TJBot respond by voice
• Provide a response to a user’s natural language input
• Extract intents and entities trained in Watson Assistant

Question 4: TJBot can recognize the verbal emotions by using the method analyzeTone.

• True
• False

Question 5: Which methods can be chained together with the converse method? Select all that apply.

• listen
• speak
• translate
• analyzeTone

Question 6: What is the correct order of the following three arguments that are passed to the TJBot constructor?

1. Hardware, a JavaScript array of strings, i.e. speaker, microphone, camera

2. A JavaScript object with Watson service credentials

3. A JavaScript object with configuration settings like the language to speak

Select an option

• #1 #3 #2
• #1 #2 #3
• #3 #2 #1
• #2 #1 #3

Question 7: Jane wrote this code in Node.js, but the code is not working as expected. Which number points in the code identify the code that should be corrected? Select all that apply.

var TJBot = require(“tjbot”);

var tj = new TJBot( 

  [“speaker”],  // #1

  {

    listen: {

      language: “en-US” // #2  

    }

  }, 

  {

    speech_to_text: {

      username: “”,  // #3  

      password: “” // #4

    }

  });

tj.listen(function(text) { 

  console.log(text); 

});

tj.stopListening(); // #5 

• #1
• #2
• #3
• #4
• #5

Question 8: Select all the core hardware that is supported by TJBot by default.

• Raspberry Pi
• Microphone
• Thermometer
• Speaker
• Camera
• LED light
• Servo

Question 9: What is the variable workspaceId used for in the following code?

var TJBot = require(“tjbot”);

var tj = new TJBot(

  [“microphone”,”speaker”],

  {

    robot: {

      gender: “male”

    },

    speak: {

      language: “en-US”

    }

  },

  {

    speech_to_text: {

      username: “”,

      password: “”

    },

    conversation: {

      username: “”,

      password: “”

    },

    text_to_speech: {

      username: “cf63b1f3-ef18-4628-86c8-6b1871e076b9”,

      password: “MWNwz3qcdIab”

    }   

  }

);

var workspaceId = “”;

function processText(text) {

  console.log(text);

  tj.stopListening();

  tj.converse(workspaceId, text, response => {

    console.log(response);

    tj.speak(response.object.output.text.join(” “)).then(function(){

      tj.listen(processText);

    });

  });

}

tj.listen(processText);

• It segregates multiple user’s conversations with TJBot.
• It references a specific Watson Assistant service instance workspace that processes the natural language input
• It is an identifier that is used to continue the interaction with a single user over multiple calls

Question 10: Review the following code. Then, review the code blocks. Select the code block number that you must change to add in the placeholder labeled TODO to complete this program?

var TJBot = require(“tjbot”);

var tj = new TJBot(

  [“camera”,”speaker”],

  {

    robot: {

      gender: “male”

    },

    speak: {

      language: “en-US”

    }

  },

  {

    // TODO: answer the question

    text_to_speech: {

      username: “”,

      password: “”

    }

  });

tj.see(function(objects) {

  console.log(objects);

  var text = “T J Bot sees ” + objects.map(item => item[“class”]).join(“, “);

  console.log(text);

  tj.speak(text);

});   

CODE BLOCK 1

visual_recognition: {

  username: “”,

  password: “”

},

CODE BLOCK 2

visual_recognition: {

  apikey: “”

},

CODE BLOCK 3

No code changes are required. This program will run correctly without any changes.

• CODE BLOCK 1
• CODE BLOCK 2
• CODE BLOCK 3