MapReduce and YARN Cognitive Class Exam Quiz Answers

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Enroll Here: MapReduce and YARN Cognitive Class Exam Quiz Answers

MapReduce and YARN Cognitive Class Certification Answers

Module 1: Introduction to MapReduce and YARN Quiz Answers – Cognitive Class

Question 1: Which phase of MapReduce is optional?

• Shuffle
• Reduce
• Combiner
• Map

Question 2: Which node is responsible for assigning (key, value) pairs to different reducers?

• Shuffle node
• Reducer node
• Combiner node
• Mapper node

Question 3: Where are the output files of the Reducer task stored?

• A data warehouse
• Hadoop FS
• Within the Reducer node
• Linux FS

Module 2: Limitations of Hadoop v1 & MapReduce v1 Quiz Answers – Cognitive Class

Question 1: What is an issue or limitation of the original MapReduce v1 paradigm?

• It’s not scalable
• It only has one TaskTracker
• It only supports Parquet file types
• It only has one JobTracker

Question 2: How is YARN an improvement over the MapReduce v1 paradigm?

• It’s completely open source
• It splits the JobTracker into two processes: ResourceManager and ApplicationManager
• It reduces multi-tenancy to improve performance
• It splits the TaskTracker into two processes: ResourceManager and ApplicationManager

Question 3: Existing applications can run on YARN without recompilation. True or False?

• True
• False

Module 3: The Architecture of YARN Quiz Answers – Cognitive Class

Question 1: The main change from Hadoop v1 to Hadoop v2 was the consolidation of both resource management and job processing. True or False?

• True
• False

Question 2: The NodeManager is a more generic and efficient version of the TaskTracker. True or False?

• True
• False

Question 3: A new ApplicationMaster is launched for each job and ends when the job completes. True or False?

• True
• False

MapReduce and YARN Final Exam Answers – Cognitive Class

Question 1: Which of the following is the correct sequence of MapReduce flow?

• Reduce —> Combine —> Map
• Combine —> Reduce —> Map
• Map —> Reduce —> Combine
• Map —> Combine —> Reduce

Question 2: Which of the following can be used to control the number of part files in a MapReduce program’s output directory?

• Shuffle parameters
• Number of Reducers
• Counter
• Number of Mappers

Question 3: Which of the following operations will work improperly when using a Combiner?

• Average
• Maximum
• Count
• Minimum

Question 4: Which of the following is true about MapReduce?

• Compression of input files is optional.
• Output from the Map phase is replicated.
• The programmer must write the Map code, the Shuffle code, and the Reduce code.
• MapReduce programs must be written in Java.

Question 5: Input data to MapReduce is record-oriented and blocks of data contain the same number of full records. True or False?

• False.
• True.

Question 6: Which statement is true about the Reduce phase of MapReduce?

• Output results are sent to the client program.
• Data arrives from the Shuffle phase already sorted by key.
• The Reducer phase sums up the values associated with each key.
• Each Reduce task processes all the data for one key only.

Question 7: Which statement is true about the Reduce phase of MapReduce?

• Containers are used instead of slots in MRv1, and can be used with either Map or Reduce tasks in MRv2.
• There is one JobTracker in the cluster.
• MapReduce jobs written in Java for MRv1 never require recompilation.
• Each job has an ApplicationManager that obtains Container IDs from the NodeManager.

Question 8: With YARN, long-running jobs acquire and retain fixed-size containers before execution starts. True or False?

• False.
• True.

Question 9: Which of the following statements is true?

• The NameNode in Hadoop 2 is fully fault-tolerant, whereas in Hadoop 1 it was a single point of failure.
• The NodeManager in Hadoop 2 replaces the TaskTracker in Hadoop 1.
• YARN requires a minimum of two nodes, one master and one slave, to run
• Both MapReduce and YARN can scale to any cluster size

Question 10: The command athhadoop provides the CLASSPATH needed for compiling Java programs written for MapReduce or YARN. True or False?

• False.
• True.

Question 11: Which statement is true about MapReduce’s use of replication in HDFS?

• Only one copy of each replicated block is processed by MapReduce in normal operation.
• Speculative execution is normally performed on all copies of each “split.”
• Each DataNode uses RAID to store its data.
• Multiple copies of each record are kept on each node.

Question 12: On which file system (FS) is the output of a Mapper task stored?

• Linux FS, and it is replicated 3 times.
• HDFS, and it is replicated 3 times.
• Linux FS, but it is not replicated.
• HDFS, but it is not replicated.

Question 13: Which of the following statements is true?

• You can set the number of Reducers.
• The Shuffle phase is optional.
• You can set the number of Mappers and the number of Reducers.
• The number of Combiners is the same as the number of Reducers.
• You can set the number of Mappers

Question 14: What will a Hadoop job do if you try to run it with an output directory that is already present?

• It will create new files, but with a different suffix.
• It will create another directory to store the output.
• It will erase all files in that directory before running.
• It will not run.

Question 15: What are the main components of the ResourceManager in YARN? Select two.

• Scheduler
• JobTracker
• DataManager
• HDFS
• ApplicationManager

Introduction to MapReduce and YARN

MapReduce and YARN are key components in the Apache Hadoop ecosystem, designed to handle big data processing tasks efficiently and at scale.

MapReduce: MapReduce is a programming model and processing engine for distributed computing on large data sets across clusters of computers. It breaks down processing into two phases: the Map phase and the Reduce phase.

• Map Phase: In this phase, data is split into smaller chunks and processed independently in parallel across multiple nodes in a cluster. Each node performs a “map” operation, transforming the input data into intermediate key-value pairs.
• Reduce Phase: After the Map phase completes, the intermediate key-value pairs are shuffled and sorted based on their keys, then grouped together. The reduce tasks take these grouped pairs and perform a “reduce” operation on them to generate the final output.

MapReduce is fault-tolerant, meaning it can handle failures of individual nodes gracefully by redistributing tasks to other nodes.

YARN (Yet Another Resource Negotiator): YARN is a resource management layer in Hadoop that allows multiple data processing engines to run on top of the same Hadoop cluster. It decouples the resource management and job scheduling functionalities from the MapReduce programming model, making Hadoop more versatile.

YARN consists of three main components:

1. ResourceManager (RM): The ResourceManager is the master daemon responsible for managing the global assignment of resources to applications and scheduling tasks across the cluster. It maintains information about available resources and node status.
2. NodeManager (NM): Each node in the cluster runs a NodeManager, which is responsible for managing resources on that node, monitoring container resource usage, and reporting back to the ResourceManager.
3. ApplicationMaster (AM): The ApplicationMaster is a per-application framework-specific master process. It negotiates resources from the ResourceManager and works with the NodeManager(s) to execute and monitor tasks specific to the application.

YARN enables Hadoop to support various data processing frameworks beyond MapReduce, such as Apache Spark, Apache Flink, and Apache Tez, among others. This flexibility makes it easier to accommodate different types of workloads and optimize resource utilization in a Hadoop cluster.