MCQs on Introduction to Amazon EMR | AWS Amazon EMR Questions Multiple Choice
This comprehensive list of AWS Amazon EMR MCQ questions and answers is designed to enhance your understanding of Amazon Elastic MapReduce (EMR). Explore topics such as EMR overview, key use cases, and core concepts like clusters, nodes, and supported applications. Perfect for AWS certification exams and real-world learning.
AWS Amazon EMR MCQs
Overview, Use Cases, and Core Concepts
- What does EMR stand for in AWS?
a) Elastic MapReduce
b) Enhanced Machine Rendering
c) Elastic Memory Resource
d) Extended Managed Resources - Which of the following is a primary use case for Amazon EMR?
a) Running real-time gaming servers
b) Large-scale data processing
c) Hosting static websites
d) Real-time video streaming - What type of processing engine is used in Amazon EMR?
a) OLTP
b) OLAP
c) Batch and stream processing
d) Real-time transaction processing - What programming frameworks does Amazon EMR support?
a) Apache Hadoop and Spark
b) JavaScript and Node.js
c) PHP and MySQL
d) Python Flask - Which component in Amazon EMR is responsible for managing data processing tasks?
a) Cluster Manager
b) Master Node
c) Application Server
d) Compute Node - EMR is best suited for which of the following use cases?
a) IoT device management
b) Data transformation and analysis
c) Online transaction processing
d) Cloud-native app hosting
Key Components: Clusters, Nodes, and Applications
- What is a cluster in Amazon EMR?
a) A collection of EC2 instances working together to process data
b) A single virtual machine for computation
c) A set of managed databases
d) A networking framework for the cloud - Which node type in an EMR cluster is responsible for task execution?
a) Master Node
b) Core Node
c) Task Node
d) Data Node - What is the function of the master node in an EMR cluster?
a) Running worker tasks
b) Managing cluster setup and coordination
c) Storing processed data
d) Ensuring data replication - Which storage option is commonly used with Amazon EMR for input and output data?
a) Amazon S3
b) Amazon DynamoDB
c) Amazon RDS
d) AWS Glue - How is an EMR application defined?
a) A lightweight process running in the cluster
b) Software frameworks like Spark or Hive used for processing data
c) A cloud-native microservice hosted in AWS
d) A database engine for querying data - What is the purpose of a step in an EMR cluster?
a) It is a single unit of work like running a Hadoop job
b) It defines network configurations
c) It manages security policies
d) It tracks cluster billing metrics - Which of these is NOT an application supported by Amazon EMR?
a) Apache Hive
b) Apache HBase
c) TensorFlow
d) Apache Pig - How is scaling achieved in Amazon EMR clusters?
a) By changing cluster roles
b) By resizing EC2 instances manually
c) Through automatic addition/removal of nodes
d) By enabling enhanced networking - What role does YARN play in Amazon EMR?
a) It is a cluster coordination tool
b) It provides resource management for distributed applications
c) It is a machine learning framework
d) It handles network encryption
Miscellaneous
- Which AWS service is commonly integrated with EMR for querying structured data?
a) Amazon Athena
b) Amazon S3 Glacier
c) Amazon Connect
d) AWS IoT Core - What is the default storage used by EMR for temporary data during processing?
a) EBS volumes
b) Amazon RDS
c) DynamoDB tables
d) Glacier archives - How does EMR pricing work?
a) Based on the amount of data stored
b) Pay-as-you-go for the underlying EC2 instances and storage used
c) Flat-rate monthly charges
d) Based on network usage only - What is the benefit of using Spot Instances in EMR clusters?
a) Reduced data transfer latency
b) Significant cost savings for non-critical workloads
c) Improved cluster performance
d) Enhanced fault tolerance - Which AWS service provides visualization and analysis for EMR logs?
a) Amazon CloudWatch
b) Amazon SNS
c) AWS Elastic Beanstalk
d) Amazon QuickSight
Answers
| QNo | Answer (Option with Text) |
|---|---|
| 1 | a) Elastic MapReduce |
| 2 | b) Large-scale data processing |
| 3 | c) Batch and stream processing |
| 4 | a) Apache Hadoop and Spark |
| 5 | b) Master Node |
| 6 | b) Data transformation and analysis |
| 7 | a) A collection of EC2 instances working together to process data |
| 8 | c) Task Node |
| 9 | b) Managing cluster setup and coordination |
| 10 | a) Amazon S3 |
| 11 | b) Software frameworks like Spark or Hive used for processing data |
| 12 | a) It is a single unit of work like running a Hadoop job |
| 13 | c) TensorFlow |
| 14 | c) Through automatic addition/removal of nodes |
| 15 | b) It provides resource management for distributed applications |
| 16 | a) Amazon Athena |
| 17 | a) EBS volumes |
| 18 | b) Pay-as-you-go for the underlying EC2 instances and storage used |
| 19 | b) Significant cost savings for non-critical workloads |
| 20 | a) Amazon CloudWatch |