MCQs on Data Processing and Workflows | AWS Amazon EMR Questions Multiple Choice
AWS Amazon EMR (Elastic MapReduce) simplifies big data processing using frameworks like Apache Spark, Hadoop, and Presto. This set of AWS Amazon EMR MCQ questions and answers focuses on running these data processing jobs and automating workflows with AWS Step Functions. Perfect for mastering EMR concepts and exam preparation.
Running Apache Spark, Hadoop, and Presto Jobs
- What is the primary purpose of AWS EMR?
a) Hosting websites
b) Processing and analyzing big data
c) Managing cloud networking
d) Running containerized applications - Which framework in EMR is best for in-memory data processing?
a) Apache Hadoop
b) Apache Spark
c) Presto
d) HBase - What is the default file system used in EMR for storing data?
a) Amazon EFS
b) Amazon S3
c) HDFS
d) EBS - In Amazon EMR, Presto is used for:
a) Batch data processing
b) Real-time analytics
c) Interactive SQL querying
d) Managing storage volumes - Which EC2 instance type is typically used for running Apache Spark in EMR?
a) T2.micro
b) M5.large
c) R5.xlarge
d) G4dn.2xlarge - What is a primary use case for Apache Hadoop in EMR?
a) Machine learning model training
b) Batch processing of large datasets
c) Real-time streaming
d) Graphical data visualization - Which AWS service is most commonly integrated with EMR for data storage?
a) Amazon Redshift
b) Amazon S3
c) Amazon DynamoDB
d) Amazon RDS - Apache Spark in EMR provides built-in support for:
a) Data encryption
b) Stream processing with Spark Streaming
c) Interactive dashboards
d) Data warehouse management - How does EMR help in cost optimization for Spark jobs?
a) By running on-demand instances exclusively
b) By using EC2 Spot Instances
c) By restricting cluster size
d) By limiting memory allocation - Which scheduling option is supported by Hadoop MapReduce in EMR?
a) First In, First Out (FIFO)
b) Round Robin
c) Weighted Fair Scheduling
d) Job Stealing
Workflow Automation with AWS Step Functions
- AWS Step Functions integrate with EMR to:
a) Automate data workflows
b) Provide container orchestration
c) Deploy machine learning models
d) Monitor EC2 instances - In Step Functions, what is used to define a sequence of tasks?
a) JSON state machine definitions
b) Lambda scripts
c) CloudFormation templates
d) IAM policies - Which AWS service is commonly triggered by Step Functions to run data processing tasks?
a) AWS Lambda
b) Amazon DynamoDB
c) Amazon SNS
d) Amazon Aurora - How does AWS Step Functions handle failure in an EMR job workflow?
a) Automatically retries the failed step
b) Stops execution completely
c) Sends alerts to all users
d) Deletes the entire workflow - Which Step Functions feature helps control task execution order?
a) Task priority rules
b) State machine transitions
c) Parallel task execution
d) EventBridge triggers - What is a major advantage of integrating Step Functions with EMR?
a) Real-time data querying
b) Parallel job execution and orchestration
c) Automated cluster resizing
d) Data encryption at rest - When running a Step Function workflow, which language is typically used to define the workflow?
a) Python
b) YAML
c) JSON
d) XML - In a Step Function workflow, how is output from one task passed to the next?
a) Via S3 bucket triggers
b) Through state input/output mapping
c) By creating temporary EC2 instances
d) Using RDS queries - What is the primary benefit of workflow automation with Step Functions?
a) Improved storage performance
b) Reduced operational complexity
c) Increased EC2 instance count
d) Higher security for cluster operations - Which feature ensures that AWS Step Functions can manage workflows for different EMR clusters?
a) Dynamic cluster allocation
b) Asynchronous task management
c) Resource tagging
d) Multi-region workflow support
Answer Key
| Qno | Answer |
|---|---|
| 1 | b) Processing and analyzing big data |
| 2 | b) Apache Spark |
| 3 | b) Amazon S3 |
| 4 | c) Interactive SQL querying |
| 5 | c) R5.xlarge |
| 6 | b) Batch processing of large datasets |
| 7 | b) Amazon S3 |
| 8 | b) Stream processing with Spark Streaming |
| 9 | b) By using EC2 Spot Instances |
| 10 | a) First In, First Out (FIFO) |
| 11 | a) Automate data workflows |
| 12 | a) JSON state machine definitions |
| 13 | a) AWS Lambda |
| 14 | a) Automatically retries the failed step |
| 15 | b) State machine transitions |
| 16 | b) Parallel job execution and orchestration |
| 17 | c) JSON |
| 18 | b) Through state input/output mapping |
| 19 | b) Reduced operational complexity |
| 20 | d) Multi-region workflow support |