MCQs on Data Storage and Integration | AWS Amazon EMR Questions Multiple Choice

Dive into these AWS Amazon EMR MCQ questions and answers to strengthen your understanding of Data Storage and Integration. Topics include integration with Amazon S3, DynamoDB, and RDS, and the use of HDFS and EMRFS for Big Data Management. Perfect for cloud professionals and learners aiming for EMR expertise!

Chapter: Data Storage and Integration

1-10: Integration with Amazon S3, DynamoDB, and RDS

1. Which AWS service is primarily used for integrating EMR clusters with unstructured data storage?
   a) Amazon RDS
   b) Amazon DynamoDB
   c) Amazon S3
   d) AWS Glue
2. How does Amazon EMR interact with Amazon S3 for data processing?
   a) By using EMRFS to read and write data
   b) By creating backups in S3
   c) Through manual file transfers
   d) By mounting S3 as a file system
3. What is the primary benefit of integrating EMR with Amazon DynamoDB?
   a) For high-speed in-memory data processing
   b) For querying and storing key-value data efficiently
   c) For running relational database queries
   d) For creating S3-compatible storage
4. Which tool is commonly used in EMR for querying structured data in Amazon RDS?
   a) Hive
   b) Presto
   c) Sqoop
   d) Pig
5. When using Amazon S3 as storage for EMR, what ensures data consistency during processing?
   a) Consistency protocols of DynamoDB
   b) EMRFS Consistent View
   c) Amazon RDS connection pool
   d) HDFS caching
6. What does the integration of Amazon EMR with RDS allow?
   a) Real-time analytics
   b) Querying and processing relational data
   c) Data archiving in S3
   d) Parallel processing of key-value data
7. Which Amazon EMR component is specifically designed to integrate with DynamoDB?
   a) Hive
   b) HBase
   c) Spark
   d) Hadoop Streaming
8. What protocol does EMRFS use to interact with Amazon S3?
   a) REST API
   b) FTP
   c) SSH
   d) HTTP/2
9. Which feature in Amazon EMR allows direct querying of DynamoDB tables using SQL-like syntax?
   a) Presto
   b) Spark SQL
   c) HiveQL
   d) Pig Latin
10. When integrating EMR with Amazon RDS, which factor needs to be managed for optimal performance?
    a) VPC routing
    b) JDBC driver configurations
    c) S3 bucket permissions
    d) EC2 instance metadata

11-20: Using HDFS and EMRFS for Big Data Management

11. What is the role of HDFS in Amazon EMR?
    a) To manage in-memory data storage
    b) To provide distributed storage for EMR clusters
    c) To handle streaming data
    d) To create backups for Amazon RDS
12. How does EMRFS differ from HDFS?
    a) EMRFS integrates with S3, while HDFS is cluster-specific
    b) HDFS is used for small files, EMRFS for big data
    c) EMRFS provides high availability, while HDFS does not
    d) EMRFS is a database, HDFS is a query tool
13. Which storage layer is used by Amazon EMR for temporary storage during processing?
    a) Amazon S3
    b) HDFS
    c) DynamoDB
    d) RDS
14. What is the main advantage of using EMRFS over HDFS for storage?
    a) Low latency
    b) Scalability and cost-effectiveness
    c) Faster data transfer rates
    d) Built-in encryption
15. What feature of HDFS makes it ideal for big data workloads in EMR?
    a) Flat file structure
    b) Distributed and fault-tolerant design
    c) Integration with relational databases
    d) Built-in data compression
16. Which configuration file is critical for setting up HDFS in EMR?
    a) core-site.xml
    b) s3-site.xml
    c) emrfs-site.xml
    d) hive-site.xml
17. What does EMRFS Consistent View help mitigate?
    a) Inconsistent read and write operations in Amazon S3
    b) Data loss in HDFS clusters
    c) Connection errors with DynamoDB
    d) Network latency issues in RDS
18. How does EMR handle data replication in HDFS?
    a) By creating copies on S3 buckets
    b) By replicating blocks across cluster nodes
    c) By syncing data with DynamoDB
    d) By archiving data in RDS
19. What is the default replication factor for HDFS in Amazon EMR?
    a) 1
    b) 3
    c) 2
    d) 5
20. Which tool enables seamless transitions between HDFS and EMRFS in big data processing?
    a) Sqoop
    b) DistCp
    c) Pig
    d) Spark

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