MCQs on HDFS Data Migration | Hadoop HDFS

Master the essentials of HDFS Data Migration, including data ingestion using Apache Flume, integration with Apache Sqoop, inter-cluster data management, and HDFS backup and restore strategies. Test your knowledge now!

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Data Ingestion to HDFS: Using Apache Flume

1. What is Apache Flume primarily used for in HDFS data ingestion?
   • A) Querying data
   • B) Streaming large volumes of log data
   • C) Backing up files
   • D) Managing clusters
2. Which of the following is a core component of Apache Flume?
   • A) Source, Sink, and Channel
   • B) Producer and Consumer
   • C) Nodes and Mappers
   • D) Reducer and Mapper
3. In Flume, a channel acts as:
   • A) A storage buffer between the source and sink
   • B) The origin of the data stream
   • C) The endpoint for writing data
   • D) A load balancer
4. Which Flume component is responsible for writing data to HDFS?
   • A) Source
   • B) Sink
   • C) Channel
   • D) Interceptor
5. What is the purpose of an interceptor in Apache Flume?
   • A) To filter or modify events
   • B) To split the data stream
   • C) To manage channel capacity
   • D) To optimize cluster performance
6. Which type of Flume source is used to collect log data from application servers?
   • A) HTTP source
   • B) Exec source
   • C) Syslog source
   • D) Avro source
7. Which of the following is an advantage of using Flume for data ingestion?
   • A) Real-time querying capabilities
   • B) Scalability for large data streams
   • C) Data compression during transfer
   • D) Built-in SQL support
8. How does Flume achieve fault tolerance during data ingestion?
   • A) By replicating events across multiple channels
   • B) By re-sending failed events
   • C) By encrypting data streams
   • D) By using checkpoint files

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Integrating HDFS with Apache Sqoop for Data Transfer

9. What is Apache Sqoop used for in Hadoop?
   • A) Importing/exporting structured data between HDFS and relational databases
   • B) Running MapReduce jobs
   • C) Streaming unstructured data into HDFS
   • D) Performing real-time analytics
10. Which Sqoop command is used to transfer data from a database to HDFS?
    • A) sqoop-import
    • B) sqoop-export
    • C) sqoop-move
    • D) sqoop-transfer
11. In Sqoop, which parameter specifies the target directory in HDFS?
    • A) –target-path
    • B) –hdfs-dir
    • C) –warehouse-dir
    • D) –output-path
12. What is the purpose of the --split-by option in Sqoop?
    • A) Splitting files into smaller blocks in HDFS
    • B) Defining the column for parallel data transfer
    • C) Splitting large jobs into smaller tasks
    • D) Specifying the delimiter for file output
13. Which file format is not supported by Sqoop during data import?
    • A) Avro
    • B) Parquet
    • C) ORC
    • D) XML
14. How can you optimize the performance of data import using Sqoop?
    • A) By increasing the number of mappers
    • B) By using fewer reducers
    • C) By compressing source files
    • D) By enabling journaling
15. Which of the following Sqoop commands exports data from HDFS to a relational database?
    • A) sqoop-import-all-tables
    • B) sqoop-export
    • C) sqoop-data-transfer
    • D) sqoop-file-export
16. What is the role of the --connect parameter in Sqoop commands?
    • A) It specifies the database URL
    • B) It sets the HDFS location
    • C) It defines the mapper class
    • D) It provides the job configuration file

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Managing Data Migration between Hadoop Clusters

17. Which command is typically used to migrate data between Hadoop clusters?
    • A) distcp
    • B) hdfs-transfer
    • C) copyFromLocal
    • D) fs-move
18. What does the distcp tool use to ensure efficient data migration?
    • A) MapReduce framework
    • B) Spark Streaming
    • C) HDFS replication
    • D) Custom shell scripts
19. How can distcp handle large file transfers reliably?
    • A) By using retry mechanisms for failed tasks
    • B) By encrypting data during transfer
    • C) By compressing all data streams
    • D) By running in single-threaded mode
20. What is a key prerequisite for using distcp between clusters?
    • A) Both clusters must use the same HDFS version
    • B) Both clusters must share the same namenode
    • C) Both clusters must have Hive installed
    • D) Both clusters must be in the same physical location
21. Which option in distcp ensures that the source files are deleted after successful transfer?
    • A) -move
    • B) -delete-source
    • C) -rm
    • D) -clean-up
22. What is the purpose of the -update option in the distcp command?
    • A) To synchronize only changed or new files
    • B) To overwrite all files in the target cluster
    • C) To delete outdated files in the source cluster
    • D) To compress transferred files
23. Which of the following is a limitation of using distcp for data migration?
    • A) It cannot handle compressed files
    • B) It requires manual task retries
    • C) It does not support encryption
    • D) It is slower for small files

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Backup and Restore in HDFS

24. Which of the following tools can be used to back up HDFS data?
    • A) Snapshots
    • B) distcp
    • C) Hive
    • D) Pig
25. What is the primary purpose of HDFS snapshots?
    • A) To replicate data between clusters
    • B) To create a read-only point-in-time view of data
    • C) To compress HDFS files
    • D) To track file access history
26. HDFS snapshots are stored:
    • A) In the same directory as the original data
    • B) In a separate snapshot directory
    • C) In a database table
    • D) On a backup server
27. How can you restore data from an HDFS snapshot?
    • A) By copying the snapshot data to the original directory
    • B) By running the snapshot-restore command
    • C) By enabling versioning
    • D) By using a MapReduce job
28. What is a key limitation of HDFS snapshots?
    • A) They require downtime during creation
    • B) They increase storage usage significantly
    • C) They do not preserve file permissions
    • D) They cannot be deleted once created
29. Which command lists all snapshots for a given HDFS directory?
    • A) hdfs snapshot-list
    • B) hdfs snapshot-view
    • C) hdfs lsSnap
    • D) hdfs ls
30. Which strategy is recommended for ensuring data recovery in case of HDFS failure?
    • A) Periodic snapshots and offsite replication
    • B) Compressing data periodically
    • C) Upgrading the cluster hardware regularly
    • D) Using only encrypted storage

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Answer Key

Qno	Answer (Option with the text)
1	B) Streaming large volumes of log data
2	A) Source, Sink, and Channel
3	A) A storage buffer between the source and sink
4	B) Sink
5	A) To filter or modify events
6	C) Syslog source
7	B) Scalability for large data streams
8	A) By replicating events across multiple channels
9	A) Importing/exporting structured data between HDFS and relational databases
10	A) sqoop-import
11	A) –target-path
12	B) Defining the column for parallel data transfer
13	D) XML
14	A) By increasing the number of mappers
15	B) sqoop-export
16	A) It specifies the database URL
17	A) distcp
18	A) MapReduce framework
19	A) By using retry mechanisms for failed tasks
20	A) Both clusters must use the same HDFS version
21	B) -delete-source
22	A) To synchronize only changed or new files
23	D) It is slower for small files
24	A) Snapshots
25	B) To create a read-only point-in-time view of data
26	B) In a separate snapshot directory
27	A) By copying the snapshot data to the original directory
28	B) They increase storage usage significantly
29	A) hdfs snapshot-list
30	A) Periodic snapshots and offsite replication