Practice Library
All MCQs
Browse exam-wise, subject-wise, and country-wise MCQs with explanations.
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Correct Answer: A. Collecting and transporting high-volume event and log data
Explanation:
Flume uses sources, channels, and sinks to build ingestion pipelines.
It was widely used for log delivery into HDFS or HBase.
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Correct Answer: D. Transfer bulk data between relational databases and Hadoop storage
Explanation:
Sqoop parallelized imports and exports using database connectors.
It was commonly used to move structured enterprise data into Hadoop.
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Correct Answer: C. A distributed column-family database for low-latency random access on Hadoop
Explanation:
HBase stores sparse tables over HDFS and supports row-key reads and writes.
It complements HDFS's large sequential access model.
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Correct Answer: B. SQL-like querying and data warehousing on distributed storage
Explanation:
Hive provides tables, schemas, and a SQL interface over large datasets.
Queries may execute through engines such as Tez or Spark.
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Correct Answer: A. A condition where incoming data arrives faster than the system can process it
Explanation:
Backlog and latency grow when processing capacity is insufficient.
Rate control, scaling, and optimization help restore stability.
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Correct Answer: D. The destination to which processed results are written
Explanation:
Sinks include files, tables, message systems, and consoles.
Their transactional capabilities affect end-to-end guarantees.
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Correct Answer: C. It stores progress and state needed to resume after failure
Explanation:
Checkpoint data records offsets, metadata, and state-store information.
A restarted query can continue consistently from the saved location.
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Correct Answer: B. A threshold used to limit how long late event-time data are retained for stateful operations
Explanation:
Watermarks express an assumption about maximum lateness.
They allow old state to be removed while still accepting reasonably late events.
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Correct Answer: A. A bounded time range over which events are grouped or aggregated
Explanation:
Windows turn an unbounded stream into finite analytical groups.
They may be tumbling, sliding, or session based.
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Correct Answer: D. The time at which the streaming system processes an event
Explanation:
Processing time depends on ingestion delay and system load.
It is easier to use but can produce inconsistent windows for late data.
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Correct Answer: C. The time at which an event actually occurred at its source
Explanation:
Event time is embedded in or derived from each event.
It may differ from arrival and processing time because of delays.
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Correct Answer: B. Processing incoming events in a sequence of small bounded batches
Explanation:
Spark commonly groups recent records into short intervals.
This provides streaming behavior using repeated distributed batch execution.
