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All MCQs
Browse exam-wise, subject-wise, and country-wise MCQs with explanations.
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Correct Answer: A. A mapper that emits each input record unchanged
Explanation:
Identity mapping preserves the input key and value.
It can be useful when only shuffle, sort, or reduce logic is needed.
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Correct Answer: D. Keys that belong to the same reducer group must reach the same reducer
Explanation:
If grouped keys are split across reducers, no reducer sees the complete group.
The partitioner commonly uses the primary grouping fields.
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Correct Answer: C. To define the ordering of intermediate keys
Explanation:
The sort comparator controls key sequence within each partition.
It may use all fields of a composite key.
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Correct Answer: B. To define which sorted intermediate keys are treated as one reducer group
Explanation:
Grouping can ignore part of a composite key while sort order uses all fields.
This is central to secondary-sort patterns.
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Correct Answer: A. A key made from multiple fields
Explanation:
Composite keys support partitioning, grouping, and secondary sort logic.
For example, they may contain customer ID and timestamp.
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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: 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: B. Filtering one join input using keys obtained from the other input
Explanation:
A compact key set can eliminate records that cannot match.
This reduces the amount of data sent through the full join.
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Correct Answer: A. Both datasets may be shuffled across the network
Explanation:
Reduce-side joins are general but communication-heavy.
Partitioning and skew strongly affect their performance.
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Correct Answer: D. A join that groups records from multiple datasets by key at reducers
Explanation:
Mappers tag and emit records under the join key.
Reducers receive all matching records and construct joined outputs.
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Correct Answer: C. When one dataset is small enough to distribute to every mapper
Explanation:
Each mapper loads the small table and joins it with its input partition.
This avoids reducer-side network transfer for the large dataset.
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Correct Answer: B. A join performed by mappers without sending both datasets through reducers
Explanation:
Map-side joins can avoid shuffle when inputs meet suitable conditions.
One common case uses a small reference dataset cached on each mapper.
