MCQ Collection
Big Data Analytics MCQs
Practice Big Data Analytics questions with answers and explanations.
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Correct Answer: C. The directory hierarchy and file metadata
Explanation:
The namespace represents folders, file names, ownership, permissions, and block mappings.
It is maintained by the NameNode.
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Correct Answer: C. Using active and standby NameNodes to reduce namespace-service downtime
Explanation:
A standby NameNode maintains synchronized metadata and can take over.
Shared edits and failover coordination support continuity.
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Correct Answer: C. Hash the key and take the result modulo the number of reducers
Explanation:
Hash partitioning spreads keys while preserving key grouping.
Skewed key frequencies can still produce uneven reducer workloads.
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Correct Answer: C. An uneven distribution of keys or records across tasks
Explanation:
Skew causes some tasks to process much more data than others.
Stragglers then delay the entire job.
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Correct Answer: C. Probabilistically testing whether a key may exist in another dataset
Explanation:
Bloom filters use little memory and never produce false negatives.
Possible false positives are checked during the actual join.
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Correct Answer: D. The ability to add or remove resources as demand changes
Explanation:
Elastic systems adjust capacity dynamically or on demand.
This can reduce cost while meeting variable workloads.
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Correct Answer: D. To reduce metadata overhead and support efficient sequential I/O
Explanation:
Large blocks reduce the number of metadata entries for huge files.
They also match high-throughput streaming access patterns.
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Correct Answer: D. Using multiple independent NameNodes to manage separate namespaces
Explanation:
Federation scales namespace capacity and isolates workloads.
The DataNodes can store blocks for multiple block pools.
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Correct Answer: D. An optional local aggregation applied to mapper output
Explanation:
A combiner reduces intermediate data before network transfer.
It may execute zero, one, or multiple times.
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Correct Answer: D. A key associated with an exceptionally large number of values
Explanation:
A hot key can overload one reducer under ordinary partitioning.
Salting or specialized aggregation may distribute its workload.
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Correct Answer: D. Controlling the order of values associated with each reducer key
Explanation:
Composite keys and grouping comparators can sort by a secondary field.
Reducers then receive grouped records in the required internal order.
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Correct Answer: A. Dividing a dataset into smaller pieces distributed across storage or workers
Explanation:
Partitioning allows multiple nodes to process different subsets concurrently.
Good partitioning balances work and reduces data movement.
