AWS Solutions Architect Associate (SAA-C02) Exam Prep Course - 2021 UPDATED! - Amazon Elastic MapReduce (EMR)

The video tutorial explains the concept of MapReduce, focusing on distributed processing to reduce workload by mapping tasks to multiple processors. It introduces Elastic MapReduce (EMR) within AWS, which uses a managed Hadoop framework to distribute processing across clusters. The tutorial details the roles of different cluster nodes: master, core, and task nodes, and provides a use case example to illustrate the efficiency of EMR in handling large-scale compute tasks.

What is the primary purpose of MapReduce?

• To create digital maps

• To distribute processing tasks across multiple processors

• To improve internet speed

• To enhance GPS accuracy

Which AWS service is used for distributed processing across clusters?

• AWS Lambda

• Elastic MapReduce (EMR)

• Amazon S3

• Amazon RDS

What is the role of the master node in EMR?

• To store data

• To coordinate job distribution

• To perform computations

• To manage network traffic

Which type of node in EMR can both run tasks and store data?

• Data node

• Task node

• Core node

• Master node

What is the main difference between core nodes and task nodes in EMR?

• Task nodes can store data

• Task nodes can run tasks and store data, while core nodes only run tasks

• Core nodes can only store data

• Core nodes can run tasks and store data, while task nodes only run tasks

Why might you use EMR for processing large-scale compute tasks?

• To minimize data storage costs

• To process tasks faster by using multiple machines

• To improve data security

• To reduce the number of servers needed

How does EMR improve the efficiency of processing large datasets?

• By compressing data before processing

• By using a single powerful machine

• By using only task nodes for all operations

• By distributing tasks across multiple nodes