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Enroll Here: Accessing Hadoop Data Using Hive Cognitive Class Exam Quiz Answers

Introduction to Accessing Hadoop Data Using Hive

Accessing Hadoop data using Hive is a common and efficient way to interact with large datasets stored in Hadoop Distributed File System (HDFS). Hive provides a SQL-like interface to query data stored in Hadoop, making it accessible to those familiar with SQL but not necessarily with Hadoop’s complex MapReduce programming model.

Understanding Hive

Hive is a data warehouse infrastructure built on top of Hadoop. It provides:

1. SQL Interface: Users can write queries in HiveQL (Hive Query Language), which is similar to SQL, to analyze data stored in Hadoop.
2. Schema on Read: Unlike traditional databases that enforce schema at write-time, Hive allows you to apply a schema when reading data stored in various formats (like JSON, CSV, etc.) from HDFS.
3. Optimized Execution: Under the hood, Hive translates HiveQL queries into MapReduce jobs (or other execution engines like Tez or Spark SQL), optimizing data retrieval and processing.

Accessing Hadoop Data Using Hive

Here’s a basic overview of how you can start accessing Hadoop data using Hive:

1. Set Up Hive: Ensure that Hive is installed and configured on your Hadoop cluster. Hive uses HDFS to store its data, so Hadoop must be up and running.
2. Create Tables: In Hive, you define tables that map to files in HDFS. You can create tables using HiveQL, specifying the file format, columns, and other attributes.sqlCopy codeCREATE TABLE my_table ( id INT, name STRING, age INT ) ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' STORED AS TEXTFILE; This example creates a table named my_table with columns id, name, and age, where data is stored as text files in CSV format.
3. Load Data: Once tables are created, you can load data into them from files stored in HDFS.sqlCopy codeLOAD DATA INPATH '/path/to/data/file' INTO TABLE my_table; This command loads data from the specified HDFS path into the my_table table.
4. Query Data: Use HiveQL to query the data stored in Hadoop. Queries can range from simple SELECT statements to complex aggregations and joins.sqlCopy codeSELECT * FROM my_table WHERE age > 25; This example retrieves all rows from my_table where the age column is greater than 25.
5. Optimize Performance: Hive allows for performance optimization through techniques like partitioning, bucketing, and indexing. These techniques help reduce query latency and improve overall efficiency.

Conclusion

Using Hive to access Hadoop data provides a convenient and powerful way to leverage the scalability and fault-tolerance of Hadoop while providing a familiar SQL interface. It abstracts away the complexities of Hadoop’s underlying infrastructure, making big data processing more accessible to data analysts and SQL developers. As you delve deeper, you’ll discover more advanced features and optimizations that can further enhance your data querying and processing capabilities.

Accessing Hadoop Data Using Hive Cognitive Class Certification Answers

Module 1 – Introduction to Hive Quiz Answers

Question 1: Which company first developed Hive?

• Starbucks
• Facebook
• HP
• Yahoo

Question 2: Hive is a Data Warehouse system built on top of Hadoop. True or false?

• True
• False

Question 3: Which of the following is NOT a valid Hive Metastore config?

• Server Metastore
• Local Metastore
• Remote Metastore
• Embedded Metastore

Module 2 – Hive DDL Quiz Answers

Question 1: Which of the following commands will list the databases in the Hive system?

• DISPLAY ALL DB;
• SHOW ME THE DATABASES;
• DISPLAY DB;
• SHOW DATABASES;

Question 2: MAPS are a Hive complex data type. True or false?

• True
• False

Question 3: An index can be created on a Hive table. True or false?

• True
• False

Module 3 – Hive DML Quiz Answers

Question 1: LOAD DATA LOCAL means that the data should be loaded from HDFS. True or false?

• True
• False

Question 2: Which of the following commands is used to generate a Hive query plan?

• QUERYPLAN
• SHOWME
• HOW
• EXPLAIN

Question 3: Data can be exported out of Hive. True or false?

• True
• False

Module 4 – Hive Operators and Function Quiz Answers

Question 1: Which of the following is NOT a built-in Hive function?

• triplemultiple
• floor
• upper
• round

Question 2: Users can create their own custom user defined functions. True or false?

• True
• False

Question 3: Which of the following is NOT a valid Hive relational operator?

• A ATE B
• A IS NOT NULL
• A LIKE B
• A IS NULL

Accessing Hadoop Data Using Hive Final Exam Answers

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

• To provide logging support for Hadoop jobs
• To support the execution of workflows consisting of a collection of actions
• To support SQL-like queries of data stored in Hadoop in place of writing MapReduce applications
• To move data into HDFS

Question 2: Hive is SQL-92 compliant and supports row-level inserts, updates, and deletes. True or false?

• True
• False

Question 3: In a production setting, you should configure the Hive metastore as

• Remote
• Local
• Embedded
• None of the above

Question 4: The Hive Command Line Interface (CLI) allows you to

• retrieve query explain plans
• view and manipulate table metadata
• perform queries, DML, and DDL
• All of the above

Question 5: When using the Hive CLI, which option allows you to execute HiveQL that’s saved in a text file?

• hive -d
• hive -S
• hive -e
• hive -f

Question 6: Which statement is true of “Managed” tables in Hive?

• Dropping a table deletes the table’s metadata, NOT the actual data
• You can easily share your data with other Hadoop tools
• Table data is stored in a directory outside of Hive
• None of the Above

Question 7: Hive Data Types include

• Maps
• Arrays
• Structs
• A subset of RDBMS primitive types
• All of the Above

Question 8: The PARTITION BY clause in Hive can be used to improve performance by storing all the data associated with a specified column’s value in the same folder. True or false?

• True
• False

Question 9: The LOAD DATA LOCAL command in Hive is used to move a datafile in HDFS into a Hive table structure. True or false?

• True
• False

Question 10: The INSERT OVERWRITE LOCAL DIRECTORY command in Hive is used to

• copy data into an externally managed table
• load data into a Hive Table
• append rows to an existing Hive Table
• export data from Hive to the local file system

Question 11: Hive supports which type of join?

• Left Semi-Join
• Inner Join
• Full Outer Join
• Equi-join
• All of the Above

Question 12: With Hive, you can write your own user defined functions in Java and invoke them using HiveQL. True or false?

• True
• False

Question 13: Which of the following is a valid Hive operator for complex data types?

• S.x where S is a struct and x is the name of the field you wish to retrieve
• M[k] where M is a map and k is a key value
• A[n] where A is an array and n is an int
• All of the above

Enroll Here: IBM Cloud Essentials V3 Cognitive Class Exam Quiz Answers

Introduction to IBM Cloud Essentials V3

“IBM Cloud Essentials V3” refers to a foundational course offered by IBM that introduces learners to essential concepts and functionalities of IBM Cloud. This course typically covers a range of topics designed to familiarize participants with IBM’s cloud computing platform, its capabilities, and how to effectively utilize its services.

Key components often included in IBM Cloud Essentials V3 might encompass:

1. Cloud Computing Fundamentals: An overview of cloud computing concepts, including infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS).
2. Introduction to IBM Cloud: Understanding what IBM Cloud is, its architecture, and the range of services offered within the IBM Cloud ecosystem.
3. IBM Cloud Services: Exploration of specific IBM Cloud services such as compute, storage, networking, databases, AI, and analytics services. This could also include serverless computing options like IBM Cloud Functions.
4. Deployment and Management: Learning how to deploy applications and manage resources effectively within IBM Cloud. This might involve using tools like Kubernetes for container orchestration or IBM Cloud Foundry for app deployment.
5. Security and Compliance: Understanding the security features and compliance standards supported by IBM Cloud, ensuring that applications and data are protected.
6. Integration and DevOps: Exploring capabilities for integrating IBM Cloud with other services and tools, as well as best practices for DevOps workflows.
7. Billing and Cost Management: Basics of billing models and cost management within IBM Cloud, including how to optimize spending and monitor usage.
8. Use Cases and Case Studies: Real-world examples and case studies demonstrating how organizations leverage IBM Cloud for various applications and industries.

Participants in the IBM Cloud Essentials V3 course can expect to gain a solid foundational understanding of IBM Cloud, preparing them to effectively utilize its services for their own projects or within their organizations. The course is typically designed to be accessible to beginners while also offering insights that are valuable for more experienced cloud professionals looking to enhance their skills in the IBM Cloud environment.

IBM Cloud Essentials V3 Cognitive Class Certification Answers

Module 1 – Introduction to IBM Cloud Quiz Answers

Question 1: What is a cloud-based service where instead of downloading software your desktop PC or business network to run and update, you instead access an application via an internet browser?

• IaaS
• SaaS
• DBaaS
• PaaS

Question 2: Which support level provides a technical account manager?

• AdvancedPlus
• Advanced
• Premium
• Basic

Question 3: What is a resource?

• A collection of services
• A provisioned service
• An entry in the Cloud Catalog
• A service

Question 4: What account type never expires, does not require a credit card and has access to over 40 services?

• Enterprise
• Pay-as-you-go
• Subscription
• Lite

Question 5: What is a multi-zone region?

• A region achieves 99.9 availability for your apps and services.
• A region that is comprised of 2 or more zones that are independent from each other to ensure that single failure events affect only a single zone.
• A region where you can host the power cooling, compute, network, and storage resources for services and apps.
• A region that is comprised of 3 or more zones that are independent from each other to ensure that single failure events affect only a single zone.

Module 2 – Infrastructure Quiz Answers

Question 1: IBM’s Cloud Backup service is used to back up and restore data between servers in one or more data centers. Which is NOT a benefit of Cloud Backup?

• Geo-redundancy
• Management Portal
• End-to-End Data Encryption
• SDKs

Question 2: IBM’s Cloud Object Storage is a highly scalable cloud storage service. What are some of the features of Cloud Object Storage?

• SQL query support
• Encryption
• SDKs and APIs
• All of the above

Question 3: What do IBM’s Cloud Internet Services provide?

• A service to distribute traffic among your virtual machines
• Reliable and secure options for Internet facing applications by using Cloudflare
• A highly distributed platform of servers that helps minimize delays
• Secure dedicated connectivity to IBM Cloud infrastructure

Question 4: What is the benefit of a Gen2 VPC?

• Available in all data centers
• Up to 80 Gbps networking
• Always deployed to bare metal
• Virtual machines on each node

Question 5: What is the VMware Solutions Dedicated service?

• A collection of VMware based virtual machines on a Power server
• A self-service on-demand VMware cloud computing platform with VMware vCloud Director
• A secure, isolated private cloud hosted within a public cloud
• A bare metal solution with vCenter and vSphere options

Module 3 – Deploying Applications Quiz Answers

Question 1: How can the OpenShift service on IBM Cloud be deployed?

• On a VMware Solution
• Bare metal only
• Virtual server only
• Virtual server and Bare metal

Question 2: What services relate to deploying applications to the IBM Cloud Kubernetes service?

• Helm Catalog and Continuous Delivery
• Cloud Container Registry and LogDNA
• Sysdig and LogDNA
• Cloud Container Registry and Helm Catalog

Question 3: How can the IBM Cloud Kubernetes service be deployed?

• On a VMware Solution
• Bare metal only
• Virtual server only
• Virtual server and Bare metal

Question 4: To deploy a Cloud Foundry project, use which command?

• cf start
• cf deploy
• cf run
• cf push

Question 5: What IBM Cloud service is a FaaS implementation?

• OpenShift
• Kubernetes Service
• Cloud Functions
• Cloud Foundry

Module 4 – Services on IBM Cloud Quiz Answers

Question 1: What is DevOps?

• Enabling better communication between development, IT operations, and business teams.
• A set of practices that combine software development and IT operations to shorten the development lifecycle and provide high quality software
• A set of practices that combine software development and business operations to shorten the development lifecycle and provide high quality software
• Using continuous integration to deliver faster results.

Question 2: Which services does the Watson IoT Platform write to?

• Cloud Pak for Data
• MQ, Kubernetes, MongoDB
• Db2 Hosted, Object Storage, MongoDB
• Cloudant, Object Storage, Event Streams, Db2 Warehouse

Question 3: Analytics Engine is based on which two open-source analytics projects?

• Apache Hadoop and TensorFlow
• Apache Spark and Apache Kafka
• Apache Spark and Apache Hadoop
• Apache Kafka and Pytorch

Question 4: Which IBM Cloud service is all-in-one tool for easily connecting apps, integrating data, building APIs and acting on events?

• MQ
• App Connect
• Information Server
• API Connect

Question 5: Which IBM Cloud service supports multiple messaging protocols as a broker that lets you route, track and queue messages with customizable persistence levels, delivery settings and publish confirmations?

• Messages for RabbitMQ
• Event Streams
• Watson Assistant
• MQ

IBM Cloud Essentials V3 Final Exam Answers

Question 1: Which type of delivery enables the greatest amount of control for the developer?

• SaaS
• IaaS
• PaaS
• Serverless

Question 2: Which account type provides discounted pricing?

• Pay-as-you-go
• Lite
• Advanced
• Subscription

Question 3: What is one of the main features of the cost estimator?

• Provides estimates based on local currency, if available
• Export the estimate as a Word doc
• Supported by most IBM Cloud services
• Supported by IAM enabled services

Question 4: Which IBM Cloud feature gives you command line control of your cloud resources, applications and infrastructure, from any web browser?

• IBM Cloud API
• IBM Cloud SDKs
• IBM Cloud Shell
• IBM Cloud Catalog

Question 5: Compute, Storage and Network services can be grouped together as what?

• SaaS
• PaaS
• DBaaS
• IaaS

Question 6: IBM’s Cloud Object Storage is a highly scalable cloud storage service. What are some of the features of Cloud Object Storage?

• SQL query support
• Encryption
• SDKs and APIs
• All of the above

Question 7: What does IBM Cloud Direct Link provide?

• A service to distribute traffic among your virtual machines
• Reliable and secure options for Internet facing applications by using Cloudflare
• A highly distributed platform of servers that helps minimize delays
• Secure dedicated connectivity to IBM Cloud infrastructure

Question 8: What service provides virtual servers and bare metal servers with a SAN-like iSCSI storage?

• Cloud Backup
• File Storage
• Block Storage
• Object Storage

Question 9: Which third-party VMware integration assists with creating and managing disaster recovery?

• Caveonix
• Zerto
• F5 BIG IP
• Veeam

Question 10: Which compute service allows you to run virtual servers on IBM LinuxONE, getting you access to IBM Z technology?

• Hyper Protect
• Bare metal
• Power Systems
• Virtual Servers

Question 11: OpenShift 4 brought in which new features?

• Mesh Networking, Operators, CLI
• Serverless, Integrated Container Registry, Operators
• Pipelines, Mesh Networking, Admin Console
• Serverless, Pipelines, Mesh Networking, Operators

Question 12: What is the main benefit of OpenShift on IBM Cloud?

• Free tier option
• Available in only select regions
• Automatic updates and patching
• Support for old versions of OpenShift

Question 13: What IBM Cloud service is a PaaS implementation?

• OpenShift
• Kubernetes Service
• Cloud Functions
• Cloud Foundry

Question 14: How does OpenShift extend Kubernetes?

• Monitoring tools
• Projects, virtual machine support
• CLI, integrated console
• Projects, integrated console, monitoring tools

Question 15: Cloud Functions use the following concepts?

• Actions, Hooks, Combinations
• Triggers, Hooks, Sequences
• Actions, Triggers, Sequences
• Actions, Triggers, Combinations

Question 16: Which IBM Cloud service lets you add end-to-end data visualization capabilities to your application so your users can easily drag and drop to quickly find valuable insight and create visualizations on their own?

• Watson Studio
• Cloudant
• App Connect
• Cognos Dashboard

Question 17: Which Cloud Pak solution offers low code tools to consume content, automate business workflow and the implementation of business policies?

• Cloud Pak for Automation
• Cloud Pak for Integration
• Cloud Pak for Security
• Cloud Pak for Data

Question 18: Which AI lifecycle management tool is used to monitor AI models?

• Watson OpenScale
• Watson Machine Learning
• Watson Knowledge Catalog
• Watson Studio

Question 19: Which Cloud Pak is a fully integrated data and AI platform that modernizes how businesses collect, organize and analyze data to infuse AI throughout their organizations?

• Cloud Pak for Multicloud Management
• Cloud Pak for Integration
• Cloud Pak for Security
• Cloud Pak for Data

Question 20: What is the enterprise grade messaging platform that is compatible with IBM and AWS?

• App Connect
• Messages for RabbitMQ
• Event Streams
• MQ
• Related

Enroll Here: Predictive Modeling Fundamentals I Cognitive Class Exam Quiz Answers

Introduction to Predictive Modeling Fundamentals I

Predictive modeling is a powerful technique used in data science and machine learning to predict outcomes based on data. It involves building a model from historical data with known outcomes (the training data) and using this model to make predictions on new data where the outcomes are unknown.

Key Concepts:

1. Predictive Modeling: The process of creating a model that predicts the value of a target variable based on input features.
2. Target Variable: Also known as the dependent variable or response variable, this is the variable you are trying to predict.
3. Features: These are the input variables used to predict the target variable. They can be numerical, categorical, or even text data.
4. Training Data: Historical data used to train or build the predictive model. It consists of features and their corresponding known target variable values.
5. Model Building: The process of selecting an appropriate algorithm, training the model on the training data, and optimizing its parameters to achieve accurate predictions.
6. Prediction: Applying the trained model to new data to predict the target variable’s value.

Steps in Predictive Modeling:

1. Data Collection: Gather relevant data that includes both features and the target variable.
2. Data Cleaning: Preprocess the data to handle missing values, outliers, and format the data appropriately for modeling.
3. Feature Selection/Engineering: Choose relevant features that are likely to have a strong influence on predicting the target variable. This might involve transforming variables or creating new features.
4. Model Selection: Select an appropriate algorithm based on the nature of the problem (e.g., regression for continuous target variables, classification for categorical target variables).
5. Model Training: Use the training data to fit the model to learn the relationship between features and the target variable.
6. Model Evaluation: Assess the model’s performance using evaluation metrics such as accuracy, precision, recall, or mean squared error, depending on the problem type.
7. Model Tuning: Fine-tune the model by adjusting hyperparameters or trying different algorithms to improve its performance.
8. Prediction: Deploy the trained model to make predictions on new data where the target variable is unknown.

Common Algorithms Used:

• Linear Regression: For predicting continuous variables.
• Logistic Regression: For predicting categorical variables.
• Decision Trees: For both regression and classification tasks.
• Random Forests: Ensemble method based on decision trees.
• Gradient Boosting Machines (GBM): Another ensemble method that builds trees sequentially.

Applications:

• Finance: Predicting stock prices, credit risk assessment.
• Healthcare: Diagnosing diseases based on symptoms.
• Marketing: Targeted advertising and customer segmentation.
• E-commerce: Recommender systems and predicting customer behavior.

Challenges:

• Overfitting: When a model performs well on training data but fails to generalize to new, unseen data.
• Underfitting: When a model is too simplistic to capture the underlying patterns in the data.
• Data Quality: Poor quality data can lead to inaccurate predictions.
• Interpretability: Complex models like neural networks might be accurate but difficult to interpret.

Conclusion:

Predictive modeling is a crucial tool in extracting insights and making informed decisions from data. By understanding the fundamentals of predictive modeling, you can leverage data effectively to solve real-world problems across various domains.

Predictive Modeling Fundamentals I Cognitive Class Certification Answers

Module 1: Introduction to Data Mining Quiz Answers

Question 1: Which of the following applications would require the use of data mining? Select all that apply.

• Predicting the outcome of flipping a fair coin
• Determining which products in a store are likely to be purchased together
• Predicting future stock prices using historical records
• Determining the total number of products sold by a store
• Sorting a student database by gender

Question 2: Which of the following is NOT a section of the Modeler Interface?

• Nodes
• Palettes
• Stream Canvas
• Stream, Outputs, and Model Manager
• All of the above are sections of the Modeler Interface

Question 3: Which of the following is NOT a part of the Cross-Industry Process for Data Mining?

• Business Understanding
• Evaluation
• Data Preparation
• Data Storage
• Modeling

Module 2: The Data Mining Process Quiz Answers

Question 1: Which phase of the data mining process focuses on understanding the project requirements and objectives?

• Data Understanding
• Data Exploration
• Data Preprocessing
• Business Understanding
• Data Preparation

Question 2: Which Data Preprocessing task focuses on removing outliers and filling in missing values?

• Data Integration
• Data Cleaning
• Data Transformation
• Data Reduction
• None of the above

Question 3: The IBM SPSS Modeler supports which data type?

• Nominal
• Categorical
• Ordinal
• Continuous
• All of the above

Module 3: Modeling Techniques Quiz Answers

Question 1: Which of the following methods are commonly used for supervised learning tasks? Select all that apply.

• Neural Networks
• Decision Trees
• K-Means
• CARMA
• Regression

Question 2: Classification is a subset of supervised learning that focuses on modeling continuous variables. True or false?

• True
• False

Question 3: Which of the following algorithms is NOT supported by the SPSS Modeler?

• Logistic Regression
• CARMA
• K-Means
• Apriori
• All of the above algorithms are supported

Module 4: Model Evaluation Quiz Answers

Question 1: What is the term for a negative data point that is incorrectly classified as positive?

• True Negative
• False Positive
• True Positive
• False Negative
• None of the above

Question 2: Which of the following is NOT a cost-sensitive performance metric?

• Precision
• Accuracy
• Specificity
• Sensitivity
• All of the above metrics are cost-sensitive

Question 3: What is the formula for the precision metric?

• (False Positive) / (True Negative + True Positive)
• (True Positive) / (True Positive + False Positive)
• (False Positive) / (True Positive + False Positive)
• (True Positive) / (True Positive + False Negative)
• (True Negative) / (True Negative + False Positive)

Module 5: Deployment on IBM Bluemix Quiz Answers

Question 1: In general, the testing dataset should be significantly larger than the training dataset. True or false?

• True
• False

Question 2: Which of the following is NOT a model deployment solution?

• Bluemix
• CRISP-DM
• IBM Collaboration and Deployment Services
• SPSS Solution Publisher
• All of the above are model deployment solutions

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

• Bluemix generally takes about a week to deploy an app
• Bluemix is supported by a growing community
• Bluemix is closed-source
• Bluemix provides a self-service application-hosting environment
• Bluemix provides built-in load-balancing capabilities

Predictive Modeling Fundamentals I Final Exam Answers

Question 1: Which of the following suggests that the model is overfitting the data?

• High accuracy on training data and high accuracy on testing data
• Low accuracy on training data and high accuracy on testing data
• Low accuracy on training data and low accuracy on testing data
• High accuracy on training data and low accuracy on testing data
• None of the above

Question 2: Which of the following tasks would require the use of data mining?

• Predicting the outcome of rolling two fair dice
• Determining which products in a store are likely to be purchased together
• Sorting a customer database by age
• Computing the number of products sold over a given time period
• All of the above

Question 3: Suppose you have collected data on your customers and you wish to determine the demographics they fall into. Which technique is best suited for this task?

• Neural Network
• Logistic Regression
• Clustering
• Linear Regression
• Decision Tree

Question 4: Suppose you wish to use data mining in order to determine which customers are most likely to sign up for a new service. Which technique is best suited for this task?

• Apriori
• Decision Tree
• Sequence
• K-means
• CARMA

Question 5: Which SPSS Modeler node can be used to determine a model’s performance? Select all that apply.

• Evaluation Node
• Analysis Node
• Table Node
• Auto Classifier Node
• Sequence Node

Question 6: Which of the following is NOT a classification or prediction algorithm in SPSS Modeler?

• Linear Regression
• Neural Network
• Logistic Regression
• Discriminant
• Apriori

Question 7: Which SPSS Modeler node is used to specify whether a given field is an input or a target?

• Auto Classifier Node
• Data Audit Node
• Table Node
• Type Node
• Analysis Node

Question 8: Which SPSS Modeler node is useful for exploratory analysis on a data set?

• Analysis Node
• Auto Classifier Node
• Table Node
• Data Audit Node
• Evaluation Node

Question 9: Which SPSS Modeler node is used to both rename fields and exclude fields from the model?

• Restructure Node
• Filter Node
• Partition Node
• Data Audit Node
• Evaluation Node

Question 10: What is the formula for the accuracy metric? TP = true positive, TN = true negative, FP = false positive, and FN = false negative.

• TN / (TN + FP)
• TP / (TP + FN)
• TP / (TP + FP)
• (FP + FN) / (TP + TN + FP + FN)
• (TP + TN) / (TP + TN + FP + FN)

Question 11: Which major data preprocessing step focuses on feature selection and feature extraction?

• Data Integration
• Data Cleaning
• Data Reduction
• Data Audit
• Data Transformation

Question 12: Which SPSS Modeler node is used to identify missing data and screen out potentially problematic fields?

• Auto Data Preparation Node
• Auto Classifier Node
• Restructure Node
• Evaluation Node
• Data Audit Node

Question 13: SPSS Modeler provides automated tools that determine the best algorithm to use for an application. True or false?

• True
• False

Question 14: Which SPSS Modeler node is used for sampling the data set?

• Type Node
• Partition Node
• Data Audit Node
• Filter Node
• Restructure Node

Question 15: Which phase of the data mining process focuses on gathering insights about the data set?

• Data Integration
• Data Understanding
• Business Understanding
• Data Preparation
• Data Preprocessing

Enroll Here: Controlling Hadoop Jobs using Oozie Cognitive Class Exam Quiz Answers

Introduction to Controlling Hadoop Jobs using Oozie

Oozie is a workflow scheduler system to manage Apache Hadoop jobs. It allows you to define a workflow of dependent jobs, which can be Hadoop MapReduce jobs, Hive queries, Pig scripts, and others, and schedule their execution. Here’s a basic introduction to controlling Hadoop jobs using Oozie:

1. Workflow Definition

Oozie allows you to define workflows using XML. A typical workflow consists of a sequence of actions. Each action can represent a Hadoop job or a task to be executed. Example actions include:

• Hadoop MapReduce Action: Executes a MapReduce job.
• Pig Action: Executes a Pig script.
• Hive Action: Executes a Hive query.
• Shell Action: Executes a shell script.

2. Workflow Control Nodes

In Oozie, workflows are defined using control nodes that define the workflow structure:

• Start Node: Entry point of the workflow.
• End Node: Marks the end of the workflow.
• Action Nodes: Define individual actions to be executed. These nodes specify what action to execute and any input/output specifications.
• Decision Nodes: Allow conditional execution paths based on the result of a preceding action.
• Fork and Join Nodes: Enable parallel execution of actions.

3. Workflow Configuration

Each action in an Oozie workflow requires configuration specific to the type of action. For example, a MapReduce action needs to specify the jar file, input/output paths, and other job-specific properties. This configuration is defined in the XML workflow definition.

4. Coordinator and Bundle Applications

• Coordinator Application: Allows you to schedule and manage recurrent workflows based on time and data availability.
• Bundle Application: Helps in managing workflows that are dependent on each other or need to be executed together.

5. Workflow Execution

Once a workflow is defined and configured, you can submit it to Oozie for execution. Oozie coordinates the execution of actions based on the workflow definition. It monitors the progress of actions and handles retries in case of failures.

6. Oozie CLI and Web UI

• Oozie CLI: Provides command-line tools (oozie command) to interact with Oozie, submit workflows, check status, and manage jobs.
• Oozie Web UI: Web-based interface to monitor workflows, view job history, and manage coordinator and bundle applications.

7. Error Handling and Notifications

Oozie provides mechanisms for error handling and notifications:

• Actions can specify retry policies.
• You can define email notifications on job completion or failure.

Conclusion

Oozie provides a powerful framework for managing and coordinating Hadoop jobs through workflows. It simplifies the task of scheduling and monitoring complex job dependencies and executions. By defining workflows and coordinating actions, Oozie helps in efficiently managing data processing tasks in a Hadoop environment.

Controlling Hadoop Jobs using Oozie Cognitive Class Certification Answers

Module 1 – Introduction of Oozie Workflows Quiz Answers

Question 1: Oozie definitions written in the Hadoop Process Definition Language (hPDL) are encoded in which of the following files?

• workflow.txt
• workflow.html
• workflow.json
• workflow.xml

Question 2: Oozie detects job completion via callback and polling. True or false?

• False
• True

Question 3: The Oozie expression language (EL) provides access to all of the following except

• error codes
• workflow job size
• application name
• workflow job id

Module 2 – Oozie Coodinator Quiz Answers

Question 1: Which of the following can trigger the start of an Oozie job?

• The Oozie CLI
• Data
• An application call to the API
• Time
• All of the above

Question 2: The Oozie coordinator works with Central European Time (CET). True or false?

• False
• True

Question 3: The Coordinator Job uses all of the following files except

• job.properties
• coord-config-default.xml
• coordinator.properties
• coordinator.xml

Module 3 – BigInsights Workflow Editor Quiz Answers

Question 1: Which of the following statements about the BigInsights Workflow Editor is correct?

• It displays a read-only diagram to show the overall workflow
• It runs in an Eclipse environment
• It supports complex Oozie workflows without requiring knowledge of the Oozie xml xds schema
• It’s a new feature, and it was introduced to BigInsights in version 2.0
• All of the above

Question 2: You can use the BigInsights Workflow Publishing Wizard as a graphical tool to create and modify a workflow.xml file. True or false?

• False
• True

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

• The InfoSphere BigInsights Tool for Eclipse is essentially an Eclipse module with BigInsights add-ins.
• At a higher level, we can link multiple applications to run in sequence.
• We cannot build sub-workflows in a workflow.
• Deployed applications can be scheduled.

Controlling Hadoop Jobs using Oozie Final Exam Answers

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

• To provide logging support for Hadoop jobs
• To support the execution of workflows consisting of a collection of actions
• To support SQL access to relational data stored in Hadoop
• To move data into HDFS

Question 2: How are Oozie workflows defined?

• Using the Java programming language
• Using JSON
• Using a plain text file that defines the graph elements
• Using hPDL

Question 3: Control nodes in an Oozie Workflow can contain all of the following except

• Start
• Fork
• Pig
• End
• Kill

Question 4: A workflow job can be executed from

• A Java API
• A Web-server API
• The command line
• All of the Above

Question 5: Where do the workflow.xml, config-default.xml, JAR, and .so files need to be stored prior to Oozie workflow job execution?

• On a web-server
• In HDFS within a defined directory structure
• On the local file system where you are executing the job
• None of the above

Question 6: What is the purpose of the Oozie Coordinator?

• To invoke workflows when some external event occurs
• To invoke workflows when data becomes available
• To invoke workflows at regular intervals
• All of the above

Question 7: Which of the following need to be stored in HDFS?

• coordinator.xml only
• coord-config-default.xml only
• coordinator.properties only
• coordinator.xml and coord-config-default.xml only
• coordinator.xml and coordinator.properties only

Question 8: The Oozie coordinator can be executed from

• A Java API
• A Web-server API
• The command line
• All of the Above

Question 9: How is an Oozie coordinator configured?

• Using the Java programming language
• Using JSON
• Using a plain text file that defines the workflow schedule
• Using XML

Question 10: By defining a dataset template as part of the coordinator.xml file, you can use the coordinator to trigger a workflow when an updated dataset has arrived in HDFS. True or false?

• True
• False

Question 11: coordinator.properties can be used to establish

• values for variables used in workflow.xml
• values for variables used in coordinator.xml
• the location of the coordinator job in HDFS
• All of the above

Question 12: job.properties can be used to establish

• The location of the workflow job in HDFS, only
• Values for variables used in workflow.xml, only
• The actions to perform at each stage of the workflow, only
• Values for variables used in workflow.xml, and the actions to perform at each stage of the workflow
• The location of the workflow job in HDFS, and values for variables used in workflow.xml

Question 13: The kill node is used to indicate a successful completion of the Oozie workflow. True or false?

• True
• False

Question 14: The join node in an Oozie workflow will wait until all forked paths have completed. True or false?

• True
• False

Question 15: Decision nodes can be used to select from multiple alternative paths through an Oozie workflow. True or false?

• True
• False

Enroll Here: Digital Analytics & Regression Cognitive Class Exam Quiz Answers

Introduction to Digital Analytics & Regression

Digital Analytics

Digital analytics involves the collection, measurement, analysis, and interpretation of data related to digital properties such as websites, mobile applications, and other online platforms. It aims to understand user behavior, optimize performance, and drive business goals through data-driven insights.

Key Aspects:

1. Data Collection: Gathering data from various digital sources using tools like Google Analytics, Adobe Analytics, etc.
2. Data Measurement: Quantifying user interactions, traffic sources, conversions, and other metrics relevant to business objectives.
3. Data Analysis: Analyzing trends, patterns, and anomalies within the collected data to extract actionable insights.
4. Data Interpretation: Drawing conclusions and making data-driven decisions to improve digital strategies and user experiences.

Regression Analysis

Regression analysis is a statistical technique used to model the relationship between a dependent variable (response variable) and one or more independent variables (predictors). It is commonly used for understanding the impact of variables on outcomes and for making predictions.

Key Aspects:

1. Types of Regression: Includes linear regression (for linear relationships), logistic regression (for binary outcomes), polynomial regression (for non-linear relationships), etc.
2. Model Building: Selecting appropriate variables, fitting the regression model, and assessing its validity.
3. Interpretation: Understanding the coefficients (slope) of the model to interpret the relationships between variables.
4. Prediction: Using the model to predict future outcomes or behaviors based on new data.

Integration of Digital Analytics and Regression

Digital analytics often utilizes regression analysis to:

• Optimize Marketing Campaigns: Predicting user behavior based on historical data to optimize ad spend.
• User Segmentation: Identifying user segments based on behaviors to personalize experiences.
• Conversion Rate Optimization: Understanding factors influencing conversions to improve website or app design.
• Performance Measurement: Quantifying the impact of digital initiatives on business metrics.

Challenges: Incorporating regression in digital analytics requires handling large, often complex datasets and ensuring the validity of assumptions such as linearity and independence of variables.

In summary, digital analytics leverages regression analysis to extract insights from data and drive informed decisions in the digital landscape. Understanding both disciplines is crucial for professionals aiming to harness data for strategic advantage in digital marketing, e-commerce, and beyond.

Digital Analytics & Regression Cognitive Class Certification Answers

Module 1: Case Study ‘CEO vs. CMO’ Quiz Answers

Question 1: You are a newly-hired analyst at a small tech startup in a big metropolitan city. In your first team meeting, and one week before the scheduled product launch, the Chief Executive Officer (CEO) and the Chief Marketing Officer (CMO) have a heated argument about what to call the product.

The CEO has done a quick search on Google Trends and found that ‘Analytics’ is, by far, a more popular search term than ‘Data Science’ or ‘Data Scientist’. The CMO, who has some experience in SEM from his work at another tech company, has a gut feeling that ‘Data Scientist Workbench’ will bring the right target group to the new product. The two executives ask you to weigh in.

Based on the Case Study you have read, which of the following is the most suitable data set to start an analysis to help the executives decide which term to include in the product name?

• Existing Company Adwords Data
• Open Data
• Search Engine Trends Data
• Survey Data

Question 2: Based on the Case Study you have read, how many domains available for purchase?

• 1
• 2
• 3
• 4

Question 3: Based on the Case Study you have read, what was the ‘Expert Tip’?

• Don’t offer your opinion to senior executives if you are new to a company.
• Your first assumption is always the correct one.
• If it can’t be measured, it doesn’t exist.
• When solving business problems with data, be curious.

Module 2: Importing Google Trends data in R Quiz Answers

Question 1: How many rows does the function head(dataset) return?

Enter your answer below: 6

Question 2: What does head(case_table$WeekID) do?

Enter your answer below:

• returns the first six rows of case_table and all of its columns
• returns all the data in case_table
• returns the first six items of the WeekID column in case_table
• returns the entire WeekID column of data in case_table

Question 3: TRUE or FALSE? The function str() can be used to tell you the number of rows and columns in a dataframe, and some other characteristics of the data.

• True
• False

Module 3: Plotting & Correlation Quiz Answers

Question 1: Which search term has the most number of searches?

• Machine Learning
• Data Scientist
• Analytics
• Data Science
• Regression

Question 2: A correlation score of 0.9 between variables X and Y indicate which of the following?

• As X increases, Y decreases.
• There is a weak positive correlation between X and Y.
• X and Y are opposites of each other.
• A strong positive correlation.
• X causes Y.

Question 3: In the R script file, which command can you use to plot line graphs?

• makePlot( )
• createGraph( )
• plot( )
• linegraph( )
• graph( )

Module 4: Simple Linear Regression in R Quiz Answers

Question 1: What is the correlation between searches for ‘data science’ and ‘data scientist’? Give the numeric answer to two decimal places.

Question 2: Running a linear model in R for a dataset results in the formula: Y = 5.0 + 3.0X. Which of the following is true?

• Y will always be positive.
• X is 3 times as large as Y.
• There is a negative correlation between X and Y.
• As X increases, Y decreases.
• When X is equal to 5, the model predicts that Y will equal 20.

Question 3: In R, what symbol or character do we use to specify a column in a table?

• ?
• ==
• /
• $

Digital Analytics & Regression Final Exam Answers

Question 1: What is the correlation (as a %) between the two search terms in the exam data set? For example, if the correlation is 0.5, enter 50 below.

Question 2: What is the mean of the values for Hadoop searches? Your answer should include two decimal places (for example, 10.01).

Question 3: What is the R Squared for the Regression model in the exam data set? The answer format must be a percentage with no decimal places. For example, if the R^2 value is 0.5, then you should write 50 below.

Question 4: What weeks correspond to WeekID 16?

• 2004-06-06 – 2004-06-12
• 2004-01-25 – 2004-01-31
• 2004-04-18 – 2004-04-24
• 2004-08-15 – 2004-08-21

Question 5: Which R function creates a linear model?

• lm ( )
• abline ( )
• regression ( )
• summary ( )

Question 6: Determine the formula for your linear model. Using the linear model you have just created to predict search index values: if the search index for ‘Hadoop’ is 35, what is the predicted corresponding search index for ‘Big Data’ according to your model?

• 88
• 19
• 45
• 30

Question 7: Generally speaking, the simple linear model created from these two search terms fits the data well, TRUE/FALSE?

• TRUE
• FALSE

Question 8: In the Linear Model, ‘Hadoop’ is the Y variable, TRUE/FALSE?

• TRUE
• FALSE

Question 9: Having now gone through the exercises in this course, and going back to the debate between the CEO vs. CMO, what product name was chosen as the most suitable for the target audience?

• Analytics Workbench
• Data Scientist Workbench

Enroll Here: Accelerating Deep Learning with GPUs Cognitive Class Exam Quiz Answers

Introduction to Accelerating Deep Learning with GPUs

Accelerating deep learning with GPUs has revolutionized the field by significantly speeding up training times for complex neural networks. In this introduction, we’ll explore how GPUs (Graphics Processing Units) have become indispensable tools for researchers and practitioners in deep learning.

What are GPUs?

GPUs were originally designed for rendering graphics in computer games and simulations, where they excel at performing many parallel computations simultaneously. This parallel processing capability makes GPUs highly suitable for accelerating deep learning algorithms, which involve millions of matrix operations during both training and inference phases.

Why GPUs for Deep Learning?

1. Parallelism: Neural networks are composed of layers of interconnected nodes, each performing computations on input data. GPUs can execute these computations concurrently across thousands of cores, vastly outperforming CPUs in terms of speed for matrix operations.
2. Speed: Training deep learning models on CPUs can be prohibitively slow, sometimes taking weeks or even months for large datasets. GPUs can reduce this time to hours or days, depending on the complexity of the model and the size of the dataset.
3. Accessibility: Over the past decade, GPU technology has become more accessible and affordable. Companies like NVIDIA have developed specialized GPUs (such as the Tesla and GeForce series) that cater specifically to the needs of deep learning practitioners.

GPU-Accelerated Libraries and Frameworks

Several libraries and frameworks have emerged that leverage GPUs for deep learning tasks:

• CUDA (Compute Unified Device Architecture): Developed by NVIDIA, CUDA is a parallel computing platform and programming model that allows developers to use GPUs for general-purpose processing.
• cuDNN (CUDA Deep Neural Network library): A GPU-accelerated library of primitives for deep neural networks. cuDNN provides highly optimized implementations for standard routines like convolution, pooling, and activation functions.
• TensorFlow and PyTorch: Two popular deep learning frameworks that have GPU support built-in. They allow seamless integration with CUDA and cuDNN, enabling users to effortlessly harness the power of GPUs for training and inference.

Practical Considerations

When setting up a deep learning environment with GPUs, considerations include:

• Hardware: Choosing the right GPU(s) based on memory size, compute capabilities, and cost.
• Software: Installing appropriate drivers, CUDA toolkit, and deep learning frameworks that support GPU acceleration.
• Performance Optimization: Ensuring that your model and data processing pipelines are optimized to fully utilize GPU resources.

Conclusion

In summary, GPUs have revolutionized deep learning by providing unprecedented speed and efficiency in training neural networks. As deep learning models continue to grow in size and complexity, the role of GPUs will only become more critical in enabling breakthroughs across various domains, from computer vision to natural language processing and beyond.

Accelerating Deep Learning with GPUs Cognitive Class Certification Answers

Module 1 – Intro to Deep Learning Quiz Answers

Question 1: Which are applications of deep learning in the industry?

• In Security: face recognition and video surveillance
• In Media: entertainment and news
• In Communications: internet service and mobile phones industries
• All of the above

Question 2: Which is NOT one of the main phases of a deep learning pipeline?

• Preprocessing input data
• Feature selection
• Training the deep learning model
• Inference and Deployment of the model

Module 2 – Hardware Accelerated Deep Learning Quiz Answers

Question 1: Which one of the following statements is NOT TRUE about GPU?

• The GPU parallelism feature reduces the computation time of the dot product of big matrices.
• GPUs are faster than CPUs in loading small chunks of data.
• GPUs are very good where the same code runs on different sections of the same array.
• GPUs are the proper use for parallelism operations on matrices.

Question 2: “GPUs have many cores, sometimes up to 1000 cores, so they can handle many computations in parallel.” Is this statement TRUE or FALSE?

• TRUE
• FALSE

Module 3 – Deep Learning in the Cloud Quiz Answers

Question 1: Which are the most popular hardware accelerators in use today?

• FPGAs (programmable or customizable hardware)
• AMD cards with OpenCL software
• Tensorflow Processing Units (TPUs)
• NVIDIA GPUs with CUDA software
• All of the above

Question 2: “In some situations, your data might be very huge in terms of volume and computation in such a way that you need a really large computational system to handle it. In this case, you need a cluster of GPUs to distribute the whole computational workload.” Is this statement TRUE or FALSE?

• TRUE
• FALSE

Question 3: Which of the following statement is TRUE about deep learning in the cloud?

• Building cloud-based deep learning could be really costly if you need to train models for more than 1000 hours.
• You need to analyze your data on-premise, when your data is sensitive and you may not feel comfortable to upload it into public clouds.
• If your data is big, use a fast enough single GPU to do experiments with sample data to verify many things before going full scale.
• All of the above.

Module 4 – Advanced Deep Learning Quiz Answers

Question 1: “The main objective of Distributed Deep Learning is to distribute the workload of deep learning on multiple GPUs on a node.” Is this statement TRUE or FALSE about DDL?

• TRUE
• FALSE

Question 2: Which of the following statement is TRUE about using DDL (Distributed Deep Learning) for training a model?

• DDL is comprised of a bunch of software algorithms that provide the parallelization of computation across hundreds of GPU accelerators attached to dozens of servers.
• DDL distributes deep learning training across large numbers of servers.
• DDL reduces training times for large models with large data sets.
• All of the above.

Question 3: What are the four workspaces in IBM PowerAI Vision?

• My Data Sets, My DL Tasks, My Trained Models, My Web APIs
• My Pictures, My Documents, My Downloads, My Data Sets
• My Computer, My Desk, My Chair, My Tasks
• My DL Tasks, My Trained Models, My Pictures, My Website

Question 4: After training and deploying your Object Recognition model, what format is the response of your web API?

• TXT
• JSON
• JPEG
• PNG

Question 5: What are the two types of Deep Learning tasks that IBM PowerAI Vision provides?

• Voice Recognition, Natural Language Processing
• Regression, Time Series Forecasting
• Clustering, Image Segmentation
• Classification, Object Detection

Accelerating Deep Learning with GPUs Final Exam Answers

Question 1: Which statement is NOT one of the main reasons for the increased popularity of deep learning today?

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

Question 2: What is the problem with traditional approaches for image classification?

• Extending the features to other types of images is not easy.
• The feature selection process is very ineffective.
• The process of selecting and using the best features is a time-consuming task.
• All of the above.

Question 3: Which one of the following characteristics of Convolutional Neural Network is the most important in Image Classification?

• No need to find or select features.
• Working with sound data.
• Low number of layers.
• All of the above.

Question 4: Which of the following definitions is what the “inference” part of the deep learning pipeline does?

• Finding the best feature set for classification.
• Using the trained model for classifying a new image based on its similarity to the trained model.
• Feeding an untrained network with a big dataset of images.
• Converting the images to a readable and proper format for the network.

Question 5: What is/are the main reason/s for the deep learning pipeline being so slow?

• Training a Deep Neural Network is basically a slow process.
• Building a deep neural network is an iterative process for data scientists, that is, it needs optimization and tuning and data scientists need to run it many times to make it ready to be used.
• The trained model needs to get updated sometimes, for example, because new data is added to the training set.

Question 6: Why is acceleration of the deep learning pipeline very desirable for data scientists?

• It reduces the number of pixels that the kernel should add.
• It makes the inference part of the deep learning pipeline much faster.
• It causes better feature extraction and selection.
• Data scientists can train a model more times and make it much more accurate.
• None of the above.

Question 7: Why is “training” of deep learning the most time-consuming part of the pipeline?

• There are many matrix multiplications in the process.
• Neural Networks have usually many weights, which should get updated in each iteration, and it involves expensive computations.
• Training is an iterative process.
• All of the above.

Question 8: Which one of the following statements is NOT TRUE about CPU?

• CPU is not the proper use for high parallelism.
• CPU is good at fetching big amounts of data from memory quickly.
• CPU runs tasks sequentially.
• CPU is responsible for executing a sequence of stored instructions, for example, multiplications.

Question 9: Which statement best describes GPU?

• A solution for running a Recurrent Neural Network in deep learning.
• Part of a computer system that is known as the processor or microprocessor.
• A chip (processor) traditionally designed and specialized for rendering images, animations and video for the computer’s screen.
• The core of a CPU.

Question 10: What is NOT TRUE about GPUs?

• GPUs have many cores, sometimes up to 1000 cores.
• x86 is one of the prevalent GPUs by Intel.
• GPUs can handle many computations.
• GPUs are good at fetching large amounts of memory.

Question 11: Why is GPU much better for deep learning than CPU?

• CPUs are not optimized and not the proper use for fetching high dimensional matrices.
• Deep Neural Networks need a heavy matrix for multiplication, and GPUs can do it in parallel.
• A Deep Neural Network needs to fetch input images as matrices from main memory, and GPUs are good at fetching big chunks of memory.
• All of the above.

Question 12: “NVIDIA is one of the main vendors of GPU offered with CUDA software” True or False?

• TRUE
• FALSE

Question 13: What is CUDA?

• A high-level language, which helps you write programs for NVIDIA GPU
• A software on top of AMD cards to make it faster
• A accelerating hardware that have recently succeeded in reducing the training time by several times over.
• All of the above

Question 14: Which one is NOT a hardware accelerator for training of deep learning?

• FPGAs
• AMD cards
• Tensorflow Processing Units (TPUs)
• NVIDIA GPUs
• OpenCL

Question 15: “Tensorflow Processing Units (TPUs) are Google’s hardware accelerator solution developed specifically for TensorFlow and Google’s open-source machine learning framework.” TRUE or FALSE?

• TRUE
• FALSE

Question 16: What is TRUE about the limitations of using GPUs as hardware accelerators for deep learning? (Select one or more)

• GPUs are not very fast for data parallelism, which is a must in deep neural networks.
• GPUs have a limited memory capacity (currently up to 16 GB) so this is not practical for very large datasets.
• You cannot easily buy GPUs and embed them into your local machine because of hardware dependencies and incompatibilities.
• GPUs are not compatible with CPUs.

Question 17: What are the options out there as hardware accelerators for deep learning?

• A cluster of GPUs on-premise
• GPU services provided by cloud providers
• A cluster of GPUs in the cloud
• Personal computers with an embedded GPU
• All of the above

Question 18: Is this statement about using personal computers with an embedded GPU for deep learning problems TRUE or FALSE? “A laptop with a recent NVIDIA GPU is not usually enough to solve real deep learning problems. In this case, you need to scale down the dataset or the model, which often delivers bad results.”

• FALSE
• TRUE

Question 19: What is the problem with using GPUs provided by cloud providers?

• They are properly used only for experiments with sample data to verify many scenarios before going full scale.
• You need to upload all your data on the cloud and you may not feel comfortable uploading it into public clouds.
• You cannot find services that offer multi-GPU access.
• They cannot run as fast as personal computers.

Question 20: Which statement is NOT TRUE about PowerAI:

• On the PowerAI platform, NVLink connections between GPUs reduce GPU wait time.
• PowerAI handles Big Data by transfering all data into GPUs.
• On the PowerAI platform, full NVLink connectivity between CPU and GPU allows a faster way to “reload” data into GPU.
• PowerAI takes advantage of NVLink for faster GPU-GPU communication.

Enroll Here: Simplifying data pipelines with Apache Kafka Cognitive Class Exam Quiz Answers

Introduction to Simplifying data pipelines with Apache Kafka

Apache Kafka is a powerful distributed event streaming platform designed to handle real-time data feeds with high throughput and fault tolerance. It simplifies data pipelines by decoupling producers and consumers of data through its distributed commit log architecture. Here’s an introduction to how Kafka simplifies data pipelines:

1. Distributed Messaging System: Kafka acts as a distributed messaging system where data is organized into topics. Producers publish records (messages) to topics, and consumers subscribe to these topics to process the data. This decoupling allows for asynchronous communication between components of a system.
2. Scalability and Fault Tolerance: Kafka is horizontally scalable, meaning it can handle large volumes of data by adding more brokers (nodes). It replicates data across multiple brokers for fault tolerance, ensuring that messages are not lost even if some brokers fail.
3. Real-time Data Processing: Kafka enables real-time data processing by providing low-latency message delivery. This is crucial for applications requiring timely data updates and analytics, such as financial trading platforms or real-time monitoring systems.
4. Stream Processing: Kafka Streams API allows for stream processing directly within Kafka. This simplifies the architecture by integrating data processing capabilities into the messaging system itself, reducing the need for separate stream processing frameworks.
5. Integration Flexibility: Kafka integrates well with other data systems such as databases, data lakes, and analytics platforms. This makes it easier to build end-to-end data pipelines where Kafka acts as a central hub for data ingestion and distribution.
6. Event Sourcing and Log Compaction: Kafka’s log-centric design supports event sourcing, where all changes to application state are captured as immutable events. Log compaction ensures that Kafka retains the latest value for each key in a topic, simplifying stateful stream processing.
7. Monitoring and Management: Kafka provides tools and APIs for monitoring cluster health, throughput, and latency. This helps in maintaining and optimizing data pipelines for performance and reliability.

In summary, Apache Kafka simplifies data pipelines by providing a robust and scalable infrastructure for real-time data streaming and processing. Its distributed architecture, fault tolerance, and integration capabilities make it a preferred choice for building modern data-driven applications.

Simplifying data pipelines with Apache Kafka Cognitive Class Certification Answers

Modules 1 – Introduction to Apache Kafka Quiz Answers

Question 1: Which of the following are a Kafka use case?

• Messaging
• All of the above
• Stream Processing
• Website Activity Tracking
• Log Aggregation

Question 2: A Kafka cluster is comprised of one or more servers which are called “producers”

• True
• False

Question 3: Kafka requires Apache ZooKeeper

• True
• False

Modules 2 – Kafka Command Line Quiz Answers

Question 1: There are two ways to create a topic in Kafka, by enabling the auto.create.topics.enable property and by using the kafka-topics.sh script.

• True
• False

Question 2: Which of the following is NOT returned when –describe is passed to kafka-topics.sh?

• Configs
• None of the Above
• PartitionNumber
• ReplicationFactor
• Topic

Question 3: Topic deletion is disabled by default.

• True
• False

Module 3 – Kafka Producer Java API Quiz Answers

Question 1: The setting of ack that provides the strongest guarantee is ack=1

• True
• False

Question 2: The KafkaProducer is the client that publishes records to the Kafka cluster.

• True
• False

Question 3: Which of the following is not a Producer configuration setting?

• batch.size
• linger.ms
• key.serializer
• retries
• None of the above

Module 4 – Kafka Consumer Java API Quiz Answers

Question 1: The Kafka consumer handles various things behind the scenes, such as:

• Failures of servers in the Kafka cluster
• Adapts as partitions of data it fetches migrates within the cluster
• Data management and storage into databases
• a) and b) only
• All of the Above

Question 2: If enable.auto.commit is set to false, then committing offsets is done manually, which provides gives you more control.

• True
• False

Question 3: Rebalancing is a process where group of consumer instances within a consumer group, coordinate to own mutally shared sets of partitions of topics that the groups are subscribed to.

• True
• False

Module 5 – Kafka Connect and Spark Streaming Quiz Answers

Question 1: Which of the following are Kafka Connect features?

• A common framework for Kafka connectors
• Automatic offset management
• REST interface
• Streaming/batch integration
• All of the above

Question 2: Kafka Connector has two types of worker nodes called standalone mode and centralized mode cluster

• True
• False

Question 3: Spark periodically queries Kafka to get the latest offsets in each topic and partition that it is interested in consuming form.

• True
• False

Simplifying Data Pipelines with Apache Kafka Final Exam Answers

Question 1: If the auto.create.topics.enable property is set to false and you try to write a topic that doesn’t yet exist, a new topic will be created.

• True
• False

Question 2: Which of the following is false about Kafka Connect?

• Kafka Connect makes building and managing stream data pipelines easier
• Kafka Connect simplifies adoption of connectors for stream data integration
• It is a framework for small scale, asynchronous stream data integration
• None of the above

Question 3: Kafka comes packaged with a command line client that you can use as a producer.

• True
• False

Question 4: Kafka Connect worker processes work autonomously to distribute work and provide scalability with fault tolerance to the system.

• True
• False

Question 5: What are the three Spark/Kafka direct approach benefits? (Place the answers in alphabetical order.)

Question 6: Kafka Consumer is thread safe, as it can give each thread its own consumer instance

• True
• False

Question 7: What other open-source producers can be used to code producer logic?

• Java
• Python
• C++
• All of the above

Question 8: If you set acks=1 in a Producer, it means that the leader will write the received message to the local log and respond after waiting for full acknowledgement from all of its followers.

• True
• False

Question 9: Kafka has a cluster-centric design which offers strong durability and fault-tolerance guarantees.

• True
• False

Question 10: Which of the following values of ack will not wait for any acknowledgement from the server?

• all
• 0
• 1
• -1

Question 11: A Kafka cluster is comprised of one or more servers which are called “Producers”

• True
• False

Question 12: What are In Sync Replicas?

• They are a set of replicas that are not active and are delayed behind the leader
• They are a set of replicas that are not active and are fully caught up with the leader
• They are a set of replicas that are alive and are fully caught up with the leader
• They are a set of replicas that are alive and are delayed behind the leader

Question 13: In many use cases, you see Kafka used to feed streaming data into Spark Streaming

• True
• False

Question 14: All Kafka Connect sources and sinks map to united streams of records

• True
• False

Question 15: Which is false about the Kafka Producer send method?

• The send method returns a Future for the Record Metadata that will be assigned to a record
• All writes are asynchronous by default
• It is not possible to make asynchronous writes
• Method returns immediately once record has been stored in buffer of records waiting to be sent

Enroll Here: Machine Learning – Dimensionality Reduction Cognitive Class Exam Quiz Answers

Introduction to Machine Learning – Dimensionality Reduction

Dimensionality reduction is a fundamental technique in machine learning and data science aimed at reducing the number of random variables under consideration. In essence, it simplifies the representation of data while retaining its essential features. Let’s delve into the key aspects:

What is Dimensionality Reduction?

Dimensionality reduction refers to the process of reducing the number of random variables under consideration by obtaining a set of principal variables. These variables retain essential information about the data while minimizing loss of information. It is particularly useful in high-dimensional datasets where the sheer number of variables can lead to increased computational complexity and decreased model performance.

Why is Dimensionality Reduction Important?

1. Curse of Dimensionality: High-dimensional data often suffers from the curse of dimensionality, where the volume of data increases exponentially with the number of variables, leading to sparsity and overfitting in machine learning models.
2. Computational Efficiency: Simplifying the dataset by reducing dimensions can lead to faster training times and improved performance of machine learning algorithms.
3. Visualization: By reducing data to 2 or 3 dimensions, we can visualize the data and gain insights that are not apparent in high-dimensional spaces.

Techniques of Dimensionality Reduction:

1. Principal Component Analysis (PCA): PCA is a linear technique that identifies the directions (principal components) that maximize the variance in the data. It projects the data onto a lower-dimensional subspace while retaining as much variance as possible.
2. t-Distributed Stochastic Neighbor Embedding (t-SNE): t-SNE is a non-linear technique primarily used for visualization by preserving local structure and clustering tendencies of the data in a lower-dimensional space.
3. Linear Discriminant Analysis (LDA): LDA is both a dimensionality reduction and a classification technique. It seeks to find a projection that maximizes the separability between different classes or clusters in the data.
4. Autoencoders: Autoencoders are neural network models that aim to learn a compressed representation of the input data. They consist of an encoder that maps the input into a lower-dimensional space and a decoder that attempts to reconstruct the original input from this representation.

Applications of Dimensionality Reduction:

• Image and Speech Recognition: Reduce the complexity of images and audio data for efficient processing and recognition.
• Anomaly Detection: Simplify the data while preserving anomalies for effective anomaly detection algorithms.
• Natural Language Processing: Reduce the dimensions of text data while retaining semantic information for tasks like sentiment analysis or document clustering.

In summary, dimensionality reduction plays a crucial role in preprocessing high-dimensional data, improving model performance, and aiding in data visualization. It is a powerful tool in the machine learning toolbox, offering insights and efficiencies across various domains and applications.

Machine Learning – Dimensionality Reduction Cognitive Class Certification Answers

Module 1: Data Series Quiz Answers

Question 1: Which of the following techniques can be used to reduce the dimensions of the population?

• Exploratory Data Analysis
• Principal Component Analysis
• Exploratory Factor Analysis
• Cluster Analysis

Question 2: Cluster Analysis partitions the columns of the data, whereas principal component and exploratory factor analyses partition the rows of the data. True or false?

• False
• True

Question 3: Which of the following options are true? Select all that apply.

• PCA explains the total variance
• EFA explains the common variance
• EFA identifies measures that are sufficiently similar to each other to justify combination
• PCA captures latent constructs that are assumed to cause variance

Module 2: Data Refinement Quiz Answers

Question 1: Which of the following options is true?

• A matrix of correlations describes all possible pairwise relationships
• Eigenvalues are the principal components
• Correlation does not explain the covariation between two vectors
• Eigenvectors are a measure of total variance, as explained by the principal components

Question 2: PCA is a method to reduce your data to the fewest ‘principal components’ while maximizing the variance explained. True or false?

• False
• True

Question 3: Which of the following techniques was NOT covered in this lesson?

• Parallel analysis
• Percentage of Common Variance
• Scree Test
• Kaiser-Guttman Rule

Module 3: Exploring Data Quiz Answers

Question 1: EFA is commonly used in which of the following applications? Select all that apply.

• Customer satisfaction surveys
• Personality tests
• Performance evaluations
• Image analysis

Question 2: Which of the following options is an example of an Oblique Rotation?

• Regmax
• Varimax
• Softmax
• Promax

Question 3: An Orthogonal Rotation assumes that factors are correlated with each other. True or false?

• False
• True

Machine Learning – Dimensionality Reduction Final Exam Answers

Question 1: Why might you use cluster analysis as an analytic strategy?

• To identify higher-order dimensions
• To identify outliers
• To reduce the number of variables
• To segment the market
• None of the above

Question 2: Suppose you have 100,000 individuals in a dataset, and each individual varies along 60 dimensions. On average, the dimensions are correlated at r = .45. You want to group the variables together, so you decide to run principle component analysis. How many meaningful, higher-order components can you extract?

• 60
• 3
• 20
• 24
• The answer cannot be determined

Question 3: What technique should you use to identify the dimensions that hang together?

• Principal axis factoring
• Confirmatory factor analysis
• Exploratory factor analysis
• Two of the above
• None of the above

Question 4: What are loadings?

• Covariance between the two factors
• Correlations between each variable and its factor
• Correlations between each variable and its component
• Two of the above
• None of the above

Question 5: When would you use PCA over EFA?

• When you want to use an orthogonal rotation
• When you are interested in explaining the total variance in a variance-covariance matrix
• When you have too many variables
• When you are interested in a latent construct
• None of the above

Question 6: What is uniqueness?

• A measure of replicability of the factor
• The amount of variance not explained by the factor structure
• The amount of variance explained by the factor structure
• The amount of variance explained by the factor
• None of the above

Question 7: Suppose you are looking to extract the major dimensions of a parrot’s personality. Which technique would you use?

• Maximum likelihood
• Principal component analysis
• Cluster analysis
• Factor analysis
• None of the above

Question 8: Suppose you have 60 variables in a dataset, and you know that 2 components explain the data very well. How many components can you extract?

• 45
• 5
• 60
• 2
• None of the above

Question 9: When would you use an orthogonal rotation?

• When correlations between the variables are large
• When you observe small correlations between the variables in the dataset
• When you think that the factors are uncorrelated
• All of the above
• None of the above

Question 10: When would you use confirmatory factor analysis?

• When you want to validate the factor solution
• When you want to explain the variance in the matrix accounting for the measurement error
• When you want to identify the factors
• Two of the above
• None of the above

Question 11: Which of the following is NOT a rule when deciding on the number of factors?

• Newman-Frank Test
• Percentage of common variance explained
• Scree test
• Kaiser-Guttman
• None of the above

Question 12: What is one assumption of factor analysis?

• A number of factors can be determined via the Scree test
• Factor analysis will extract only unique factors
• A latent variable causes the variance in observed variables
• There is no measurement error
• None of the above

Question 13: What is an eigenvector?

• The proportion of the variance explained in the matrix
• A higher-order dimension that subsumes all of the lower-order errors
• A higher-order dimension that subsumes similar lower-order dimensions
• A higher-order dimension that subsumes all lower-order dimensions
• None of the above

Question 14: What is a promax rotation?

• A rotation method that minimizes the square loadings on each factor
• A rotation method that maximizes the variance explained
• A rotation method that maximizes the square loadings on each factor
• A rotation method that minimizes the variance explained
• None of the above

Question 15: What is the cut-off point for the Common Variance Explained rule?

• 80% of variance explained
• 50% of variance explained
• 3 variables
• 1 unit
• None of the above

Question 16: Why would you try to reduce dimensions?

• Individuals need to be placed into groups
• Variables are highly-correlated
• Many variables are likely assessing the same thing
• Two of the above
• All of the above

Question 17: If you have 20 variables in a dataset, how many dimensions are there?

• At most 20
• At least 20
• As many as the number of factors you can extract
• Not enough information
• None of the above

Question 18: What term describes the amount of variance of each variable explained by the factor structure?

• Eigenvector
• Commonality
• Similarity
• Communality
• None of the above

Question 19: What package contains the necessary functions to perform PCA and EFA?

• ggplot2
• FA
• psych
• factAnalis
• None of the above

Question 20: What is the best method for identifying the number of factors to extract?

• Parallel Analysis
• Scree test
• Newman-Frank Test
• Percentage of common variance explained
• All of the above

Enroll Here: Building Cloud Native and Multicloud Applications Cognitive Class Exam Quiz Answers

Introduction to Building Cloud Native and Multicloud Applications

Building cloud-native and multicloud applications is pivotal in modern software development, leveraging the flexibility, scalability, and resilience offered by cloud environments. This approach focuses on designing applications specifically to harness the capabilities of cloud infrastructure, rather than merely adapting traditional software to run in the cloud.

Cloud-Native Applications

Cloud-native applications are designed to fully exploit cloud computing capabilities. Key principles include:

1. Microservices Architecture: Applications are composed of small, independent services that communicate through APIs. This enhances scalability, resilience, and enables continuous delivery.
2. Containers: Applications are packaged with all dependencies into lightweight containers. This ensures consistency across different environments, from development to production.
3. DevOps Practices: Automation, continuous integration (CI), and continuous delivery (CD) are essential. This allows rapid and reliable application updates.
4. Elasticity and Scalability: Applications can dynamically scale based on demand, leveraging cloud-native services like auto-scaling and serverless computing.
5. Resilience: Applications are designed with fault tolerance in mind, utilizing features like redundancy, graceful degradation, and quick recovery from failures.

Multicloud Applications

Multicloud applications extend the cloud-native approach by distributing workloads across multiple cloud providers. This strategy offers several advantages:

1. Vendor Lock-In Mitigation: By using multiple cloud providers, organizations reduce dependence on any single vendor and gain flexibility in pricing and service offerings.
2. Geographical Optimization: Deploying applications across different cloud regions or providers can optimize performance and reduce latency for global users.
3. High Availability: Redundancy across multiple clouds enhances application availability. Failover mechanisms can reroute traffic to an operational cloud provider in case of a failure.
4. Disaster Recovery: Multicloud strategies can improve disaster recovery capabilities by replicating data and applications across geographically dispersed cloud environments.

Building Multicloud and Cloud-Native Applications

To effectively build multicloud and cloud-native applications, consider the following steps:

1. Architecture Design: Adopt microservices and containerization to facilitate scalability and portability across clouds.
2. Service Discovery and Orchestration: Use tools like Kubernetes for container orchestration and service discovery to manage complex deployments.
3. Data Management: Implement distributed data management strategies to ensure consistency and availability across multiple cloud environments.
4. Security: Employ robust security practices, including encryption, identity management, and compliance monitoring, tailored to multicloud environments.
5. Monitoring and Governance: Utilize monitoring tools to track performance metrics and ensure compliance with organizational policies across all cloud platforms.
6. Automation: Implement automation for deployment, scaling, and management of applications to streamline operations and reduce manual effort.

By adopting these practices, organizations can effectively leverage cloud-native and multicloud architectures to achieve agility, scalability, and resilience in their applications, meeting the demands of modern digital business landscapes.

Building Cloud Native and Multicloud Applications Cognitive Class Certification Answers

Module 1 – Cloud Native and Multicloud Concepts and Goals Quiz Answers – Cognitive Class

Question 1: Which is not a characteristic of Cloud Native?

• resiliency
• agility
• dedicated
• observability

Question 2: Which is an ultimate goal of Cloud Native application design?

• horizontally scale across cloud boundaries
• vertically scale to take advantage of improved CPU, memory and disk resources
• seamlessly build, orchestrate and run highly scalable applications
• scale on-demand to meet customer needs
• All of the above

Question 3: What does CD stand for?

• Continuous Development and Delivery
• Cloud Development and Delivery
• Continuous Delivery and Deployment
• Cloud Delivery and Deployment

Question 4: Which simple definition below best describes DevOps?

• Assuring Dev and Ops teams have tooling to communicate properly
• Bringing Dev and Ops teams together to diagnose problems in production
• Bringing Dev and Ops teams together to increase application operability and end-to-end agility
• Assuring Dev and Ops teams have the same access controls on target environments

Question 5: Which is goal of Cloud Native Observability?

• Increase time-to-failure
• Optimize continuous automation
• Optimize Resources and Costs
• Increase provisioning agility

Module 2 – Migrating Apps to Advantage Cloud Infrastructure Quiz Answers – Cognitive Class

Question 1: Which is not a use case for developing Cloud Native applications?

• Optimize for server hardware
• Polyglot programming
• Flexible feature integration
• Efficient resource scaling

Question 2: Which application modernization path leverages microservices?

• Infrastructural Path
• Architectural Path
• Delivery Path
• None of the above
• All of the above

Question 3: When building applications as a Container you start with what artifact?

• An “operating system image”
• A “multi-layered image”
• A “base image”
• A “cloud native image”
• All of the above

Question 4: Which statement is true for Container images?

• New layers are automatically flattened
• Developers must add their own runtimes
• Base layers can be patched as needed
• Container images contain hardware virtualization support
• None of the above
• All of the above

Question 5: Which options below are best practices for data migration? Check all that apply.

• Only use dedicated networks
• Verify transfer impacts on business
• Transfer mission-critical data in stages
• Test for data integrity at intervals

Module 3 – Modernizing Applications to be CN Quiz Answers – Cognitive Class

Question 1: The Event Interception strategy primarily relies upon?

• “Intercepting” user invocations of APIs
• “Tapping” into events at the data services layer
• “Intercepting” function calls within the application
• “Capturing” events initiated by functions
• None of the above

Question 2: This is a popular containerization tool:

• WebSphere
• MicroProfile
• git
• Docker
• Jenkins

Question 3: In the IBM Garage Method “Envision” is trying to achieve:

• Consistent performance and SLAs are met
• Better understand the business domain
• Discover new business opportunities
• Create diverse teams and empower them to make decisions
• Creation of a reliable and repeatable approach for delivering high priority items to customers

Question 4: Which is are the functional problem with a monolithic application architecture? Check all that apply.

• High cohesion, low coupling
• High scalability
• Low Cohesion, high coupling
• Low learning curves
• All the above

Question 5: What is the name of the dashboard controlling an OpenShift cluster?

• Advanced Web Console
• WebSphere
• Kubernetes
• Helm
• Docker

Module 4 – Applying CI/CD to CN Applications Quiz Answers – Cognitive Class

Question 1: Which is a benefit of a CI/CD process?

• Cheaper
• Auditable
• Faster
• Repeatable
• All the Above

Question 2: GitOps is a type of Immutable Infrastructure Practice?

• True
• False

Question 3: In Tekton what resource maps fields from an event message?

• TriggerBinding
• ServiceAccount
• Pipeline
• EventListener
• Task

Question 4: A CI/CD process is started when?

• The daily stand-up ends
• a support ticket is created
• QA has completed their manual testing script
• a developer pushes code to a repo
• A manager gives sign-off

Question 5: Merging code into the main or production branch would happen during this phase:

• Continuous Deployment
• Continuous Delivery
• Continuous Integration
• Continuous Iteration

Module 5 – Managing Applications in Multicloud Deployments Quiz Answers – Cognitive Class

Question 1: OpenShift helps organizations operate in a multicloud environment by?

• Relaxing security barriers
• Limiting the tools available to developers and operators
• Providing a consistent dashboard and operating environment
• Adding an additional layer of complexity

Question 2: How does a Multi-Cloud Architecture improve an organization’s resiliency?

• Providing failover if a cloud provider experiences an outage
• Speed delivery to market
• Allow workloads to be run on client systems
• Give organization’s leverage during contract negotiations
• Reducing operational expenses

Question 3: Multi-Cloud Manager Uses these tools to handle the automated provisioning of cloud resources?

• COBOL, Bash, and vim
• Jenkins and C++
• Kubernetes, and Docker
• Advanced Cloud Manager, Ansible Tower, and Terraform
• Java, Docker, and Jenkins

Question 4: An organization might adopt a multi-cloud strategy for the following reason?

• Leverage unique cloud platform advantages
• Improved Multi-Region support
• Cost Savings
• Scale and Resiliency benefits
• All the Above

Question 5: Multi-Cloud Manager Cloud Pak comes with the capability of monitoring costs and resource usages across cloud platforms?

• True
• False

Building Cloud Native and Multicloud Applications Final Exam Answers – Cognitive Class

Question 1: What is not a goal of Cloud Native?

• exploit cloud orchestration and scheduling
• leverage cloud provider platform training
• exploit cloud resources and services
• leverage cloud native technologies

Question 2: Cloud Native applications can take advantage of cloud provider’s…?

• application data services
• orchestration and scheduling services
• application platform services
• converged infrastructure resources and services
• All of the above

Question 3: What phases of the Software Development Lifecycle does Cloud Native agility focus on?

• Idea
• Build
• Deploy
• Code
• All of the above

Question 4: Using scripts and declarative configuration, that are managed using CI processes, to automate the provisioning and deployment of code is called?

• Cloud Native Infrastructure
• Configuration-as-Code
• Infrastructure-as-Code
• Cloud Declarative Provisioning

Question 5: Site Reliability Engineering (SRE) practices suggests the creation of what teams separate from product delivery teams?

• DevSecOps teams
• Security teams
• Reliability teams
• Resiliency teams

Question 6: Which application modernization path utilizes automated CI/CD pipelines?

• Infrastructural Path
• Delivery Path
• Architectural Path
• None of the above
• All of the above

Question 7: Virtual Machine (VM) image sizes are typically measured in what units?

• Megabytes
• Gigabits
• Gigabytes
• Megabits
• All of the above

Question 8: Why are Virtual Machine (VM) images so large compared to Containers?

• VMs cannot be compressed or flattened
• VMs must include a hypervisor
• VMs must include a full “guest” Operating System
• VMs must always contain a backup snapshot
• All of the above

Question 9: Which popular Linux operating system distribution is also available as a base container image?

• Ubuntu
• Red Hat
• Suse
• All of the above

Question 10: Which of the following is a law or regulation designed to protect a user’s Personal Identifying Information (PII)? Check all that apply.

• United States Cloud Privacy Regulation (USCPR)
• European Union Privacy Law 2019 (EUPL2019)
• General Data Protection Regulation (GDPR)
• ISO/IEC 27017

Question 11: What are the functional characteristics of a Microservice architecture? Check all that apply.

• Highly scalable
• Independent, single purpose services
• Agile delivery of features
• High cohesion, low coupling
• All the above

Question 12: What is a best practice when using the Strangler pattern?

• Assume reason for complexity
• Start with “low hanging fruit”
• Continue to add new features to legacy app during process
• Only partially migrate
• Carry over technical debt from existing monolith

Question 13: Containers help ease deployments by:

• Adding significant overhead when running an application
• Having long startup times
• Requiring complex setup when deploying an applications
• Being difficult to share
• Packaging all an application’s runtime dependencies

Question 14: The following is a Cloud Pak offered by IBM:

• Security
• Integration
• Data
• Application
• All the above

Question 15: After being built container images are stored in a:

• Floppy Disk
• Microservice
• Email
• Database
• Container Image Repository

Question 16: Which of the following is NOT part of CI/CD?

• Continuous Integration
• Continuous Delivery
• Continuous Design
• Continuous Deployment

Question 17: How does defining a CI/CD process in code improve observability?

• An automated CI/CD process is slower than a manual one
• Code must be compiled before it can run
• An automated CI/CD process is faster than a manual one
• Code can be reviewed independent of execution

Question 18: Which of the following steps occur in Continuous Integration?

• Configure environment
• Create a release tag
• Run automated tests
• Deploy to stage
• Deploy to Production

Question 19: This tool can be used to define a GitOps process:

• ArgoCD
• Knative
• Docker
• Kubernetes
• Tomcat

Question 20: What does a unit test validate?

• A system can process a transaction
• An application can communicate with external systems
• An application’s internal behavior
• An application meets visual criteria
• An application is meeting performance requirements

Question 21: This tool in OpenShift allows additional features and functionality to be added to an OpenShift cluster?

• Kubernetes
• IBM Garage
• Operator Hub
• Aspera
• Data Cloud Pak

Question 22: Organizations can combine Hybrid-Cloud and Multi-Cloud architecture?

• True
• False

Question 23: Regulatory requirements around data storing and processing often concern?

• Who has access to the data
• Where data is processed
• The type of data stored (e.g. personally identifiable data)
• Where data is stored
• All the above

Question 24: How can a Multicloud or Hybrid-Cloud architecture help an organization meet regulatory requirements?

• Improve resiliency and scale capabilities
• Improve response time for users
• Provide greater observability
• Allow data to be stored and processed in a specific jurisdiction or geographical region
• All the Above

Question 25: A multicloud architecture can help an organization save on operational costs by?

• Improve disaster recovery capabilities
• Provide be multi-region support
• Allow for hyper-scaling of infrastructure
• Take advantage of better pricing for certain services or worloads
• All the Above

Enroll Here: SQL and Relational Databases 101 Cognitive Class Exam Quiz Answers

Introduction to SQL and Relational Databases 101

SQL is a specialized programming language used for managing and manipulating relational databases. It allows users to perform various operations on data stored in these databases, such as querying data, inserting new records, updating existing records, and deleting records. SQL is crucial for interacting with relational database management systems (RDBMS) like MySQL, PostgreSQL, SQLite, Microsoft SQL Server, and Oracle.

Relational Databases: A relational database is a type of database that stores and organizes data into tables consisting of rows and columns. These tables are related to each other based on common fields across different tables. This relational model allows for efficient organization, retrieval, and manipulation of data.

Key Concepts in SQL:

1. Tables: In SQL, data is stored in tables. Each table consists of rows (records) and columns (fields).
2. Queries: SQL allows you to retrieve data from a database using SELECT statements. Queries can be simple (selecting all rows and columns) or complex (involving conditions, joins, aggregations, etc.).
3. Inserting and Updating Data: SQL provides commands like INSERT for adding new data and UPDATE for modifying existing data within tables.
4. Deleting Data: The DELETE command is used to remove records from a table based on specified conditions.
5. Joins: SQL supports various types of joins (e.g., INNER JOIN, LEFT JOIN, RIGHT JOIN) to combine data from multiple tables based on related columns.
6. Constraints: Constraints ensure data integrity by enforcing rules on columns. Examples include UNIQUE (ensuring values are unique), NOT NULL (ensuring a column cannot have NULL values), and FOREIGN KEY (enforcing referential integrity between tables).
7. Indexes: Indexes are used to speed up data retrieval operations by providing quick access to rows based on indexed columns.
8. Transactions: SQL supports transactions, which are sequences of SQL operations that are executed as a single unit. This ensures data consistency and integrity.

Benefits of SQL and Relational Databases:

• Data Integrity: Relational databases enforce constraints to maintain data accuracy and consistency.
• Flexibility: SQL provides a flexible and powerful way to manipulate and retrieve data, supporting a wide range of operations.
• Scalability: Relational databases can handle large volumes of data and support multiple concurrent users.
• Security: They offer robust security features to protect sensitive data.

Conclusion: SQL and relational databases play a fundamental role in managing structured data efficiently. Understanding SQL allows developers and analysts to interact effectively with databases, perform complex data manipulations, and ensure data integrity. This capability is essential for applications ranging from web development to enterprise systems where structured data management is critical.

SQL and Relational Databases 101 Cognitive Class Certification Answers

Module 1 – SQL and Relational Databases 101 Quiz Answers

Question 1: What is a Database?

• A program that stores data
• Stores data in tabular form
• A repository of data
• All of the above

Question 2: Advantages of the relational model include:

• Provides logical and physical data independence
• Data is stored in simple data structures
• It is the most used data model
• All of the above

Question 3: In an Entity-Relationship diagram, the Entity Name maps to the Table name, the attributes map to the …

• Table rows and columns
• Table columns
• Table rows
• None of the above

Module 2 – Relational Model Constraints and Data Objects Quiz Answers

Question 1: Which of the following statements is true?

• A table can have a primary key and a foreign key
• A Foreign Key is a set of columns referring to a primary key of another table
• A primary key uniquely identifies each row in a table
• All of the above

Question 2: Which Relational Constraint prevents duplicate values in a table?

• Entity Integrity constraint
• Null constraint
• Check constraint
• All of the above

Question 3: The Semantic Integrity Constraint defines the relationships between tables. (T/F)

• True
• False

Module 3 – Data Definition Language (DDL) and Data Manipulation Language (DML) Quiz Answers

Question 1: The Primary Key of a relational table uniquely identifies each _____ in a table.

• column
• row
• Both of the above
• Neither of the above

Question 2: The INSERT statement cannot be used to insert multiple rows in a single statement. (T/F)

• True
• False

Question 3: The SELECT statement is called a Query, and the output we get from executing the query is called a Result Set.  (T/F)

• True
• False

Module 4 – Advanced DDL and DML Quiz Answers

Question 1: You want to select an author’s name from a table, but you only remember the author’s last name starts with the letter B, which string pattern can you use?

• SELECT lastname from author where lastname like ‘B$’
• SELECT lastname from author where lastname like ‘B%’
• SELECT lastname from author where lastname like ‘B#’
• None of the above

Question 2: In a SELECT statement, which SQL clause controls how the result set is displayed?

• ORDER BY clause
• ORDER IN clause
• ORDER WITH clause

Question 3: Which SELECT statement eliminates duplicates in the result set?

• SELECT country from author ORDER BY 1
• SELECT distinct(country) from author
• SELECT unique(country) from author
• None of the above

Module 5 – Working with Multiple Tables Quiz Answers

Question 1: An INNER JOIN returns only the rows that match. (T/F)

• True
• False

Question 2: A LEFT OUTER JOIN displays all the rows from the right table, and combines matching rows from the left table. (T/F)

• True
• False

Question 3: When using an OUTER JOIN, you must explicitly state that you want either a LEFT JOIN or a RIGHT JOIN. (T/F)

• True
• False

SQL and Relational Databases 101 Final Exam Answers

Question 1: The 5 basic SQL commands are…

• CREATE, SELECT, INSERT, UPDATE, DELETE
• CREATE, ALTER, INSERT, UPDATE, DELETE
• CREATE, SELECT, INSERT, MODIFY, DELETE
• All of the above

Question 2: The blueprint of any database system is the …

• Data model
• Information model
• Both of the above

Question 3: Attributes help clarify relationship diagrams (T/F)

• True
• False

Question 4: A table containing one or more foreign keys is called a Parent table. (T/F)

• True
• False

Question 5: The Referential Integrity Constraint ensures the validity of the data using a combination of Primary Keys and Foreign Keys.

• True
• False

Question 6: What are the basic categories of the SQL language based on functionality?

• Data Manipulation Language
• Data Definition Language
• Both of the above
• Neither of the above

Question 7: The CREATE TABLE statement is a….

• DDL statement
• DML statement
• Both of the above

Question 8: When using the UPDATE statement, if you do not specify the WHERE clause, all the rows in the table are updated.

• True
• False

Question 9: You want to select a list of books whose number of pages is between 100 and 200 . Select the correct query from the following options.

• SELECT title, pages from book where pages BETWEEN 100 AND 200
• SELECT title, pages from book where pages RANGE 100 AND 200
• SELECT title, pages from book where pages less than or equal to 100 AND pages greater than or equal to 200
• A or B
• All the options above are correct

Question 10: What is the default sorting mode of the ORDER BY clause?

• Ascending
• Descending
• Randomly selected order
• None of the above
• All of the above

Question 11: Which of the following can be used in a SELECT statement to restrict a result set?

• HAVING
• GROUP BY
• DISTINCT
• All of the above

Question 12: The HAVING clause works only with the GROUP BY clause. (T/F)

• True
• False

Question 13: Which of the following are valid types of OUTER JOIN?

• LEFT OUTER JOIN
• RIGHT OUTER JOIN
• FULL OUTER JOIN
• A and B only
• All of the above

Question 14: A FULL JOIN returns only the rows that match. (T/F)

• True
• False

Question 15: To combine tables in relational databases, we use:

• Table names
• Matching values/Primary key
• Tables cannot be combined
• None of the above options are correct

Question 16: >> True or False: Semantic integrity ensures that data entered into a row reflects an allowable value for that row.

• True
• False

Question 17: Which of the following statements are correct about databases:

• A database represents some aspect of the real world
• A database is a logically coherent collection of data with some inherent meaning
• A database is designed, built and populated with data for a specific purpose
• All the above options are correct

Question 18: Which of the following statements are correct about primary keys

• The value of the Primary Key must be unique for each instance of the entity.
• There can be no missing values (i.e., Not Null) for Primary Keys.
• The Primary Key is immutable .i.e., once created the value of the Primary Key cannot be changed.
• All the above options are correct

Question 19: The ____________ keyword is used to eliminate duplicate tuples from the result set of an SQL query.

• COMMON
• DISTINCT
• ALL
• EXISTS

Question 20: Select the correct statements about the join operator

• Primary Key- Foreign Key is the common join operator
• Is used to combine more than one table
• You have to know the relationship between the tables
• None of the Statements are correct