Controlling Hadoop Jobs using Oozie Cognitive Class Exam Quiz Answers

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

Controlling Hadoop Jobs using Oozie Cognitive Class Certification Answers

Module 1 – Introduction of Oozie Workflows Quiz Answers – Cognitive Class

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 – Cognitive Class

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 – Cognitive Class

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 – Cognitive Class

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

Introduction to Controlling Hadoop Jobs using Oozie

Oozie is a workflow scheduler system used to manage Apache Hadoop jobs. It allows users to define a workflow of dependent jobs, where each job can be a MapReduce, Pig, Hive, Sqoop, or even a custom Java application. Oozie facilitates coordination and management of these jobs, providing features like job scheduling, dependency management, and error handling. Here’s a brief introduction to controlling Hadoop jobs using Oozie:

1. Workflow Definition: In Oozie, workflows are defined using XML files. These files specify the sequence of actions, their dependencies, and configuration parameters. Actions can be Hadoop MapReduce jobs, Pig scripts, Hive queries, or any other Hadoop ecosystem component.
2. Coordinator: Oozie also provides a coordinator engine, which allows users to define workflows that run at specified times or intervals. Coordinators are useful for managing recurring data processing tasks, such as daily ETL jobs or hourly data aggregations.
3. Actions: Each step in an Oozie workflow is called an “action”. Actions can be of various types, such as:
   • MapReduce Action: Executes a Hadoop MapReduce job.
   • Pig Action: Executes a Pig script.
   • Hive Action: Executes a Hive query.
   • Shell Action: Executes a shell command.
   • SSH Action: Executes a command on a remote machine via SSH.
   • DistCp Action: Executes a Hadoop distributed copy operation.
   • Email Action: Sends an email notification.
4. Control Flow: Oozie allows users to define complex control flows within workflows. Actions can be executed sequentially, in parallel, or based on conditions. For example, a subsequent action can depend on the successful completion of a previous action, or multiple actions can run concurrently.
5. Error Handling: Oozie provides mechanisms for handling errors and retries within workflows. Users can specify retry policies for actions and define error transitions to handle failures gracefully.
6. Deployment and Execution: Once the workflow and coordinator definitions are created, they can be deployed to the Oozie server for execution. Oozie provides a REST API and a web-based user interface for managing workflows, monitoring job status, and viewing execution logs.

Controlling Hadoop jobs using Oozie offers a centralized and efficient way to manage data processing workflows, enabling users to automate and schedule complex tasks in a Hadoop ecosystem.