Practice Library
All MCQs
Browse exam-wise, subject-wise, and country-wise MCQs with explanations.
Choose an option to check your answer.
Correct Answer: A. The framework does not guarantee that a combiner will run
Explanation:
A combiner is an optimization rather than a required semantic stage.
Job correctness must not depend on its execution.
Choose an option to check your answer.
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.
Choose an option to check your answer.
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.
Choose an option to check your answer.
Correct Answer: B. The partitioner
Explanation:
The partitioner maps keys to reducer partitions.
A correct partitioner sends every identical key to the same reducer.
Choose an option to check your answer.
Correct Answer: A. Intermediate keys are ordered and grouped for each reducer
Explanation:
Sorting ensures that all values for the same key appear together.
Reducers receive keys in sorted order within their partition.
Choose an option to check your answer.
Correct Answer: D. Transferring mapper output partitions to the appropriate reducers
Explanation:
Shuffle moves intermediate records across the cluster by partition.
It can be one of the most network-intensive stages.
Choose an option to check your answer.
Correct Answer: C. Processes all intermediate values associated with a key
Explanation:
The framework groups mapper output by key before reduction.
The reducer aggregates or transforms each key's values.
Choose an option to check your answer.
Correct Answer: B. Transforms input key-value pairs into intermediate key-value pairs
Explanation:
The map function processes input records independently.
It emits zero or more intermediate pairs for later grouping.
Choose an option to check your answer.
Correct Answer: A. Key-value pairs
Explanation:
MapReduce represents input and output records as key-value pairs.
This common abstraction supports many data formats and algorithms.
Choose an option to check your answer.
Correct Answer: D. The scheduler can exploit data locality when assigning tasks
Explanation:
Tasks can run on or near nodes holding the required blocks.
This reduces expensive network transfer.
Choose an option to check your answer.
Correct Answer: C. Its design favors large immutable or append-oriented files rather than in-place mutation
Explanation:
HDFS prioritizes throughput and fault-tolerant block storage.
Systems such as HBase are better suited to random record access.
Choose an option to check your answer.
Correct Answer: B. Gracefully removing a DataNode while replicating its blocks elsewhere
Explanation:
Decommissioning preserves the target replication factor before the node leaves.
It is safer than abruptly shutting down a storage node.
