MCQ Collection
Big Data Analytics MCQs
Practice Big Data Analytics questions with answers and explanations.
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Correct Answer: D. Only a small set of local candidates reaches the final reducer
Explanation:
Each mapper keeps only its best N records.
The final reducer processes at most roughly N candidates per mapper.
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Correct Answer: A. NameNode
Explanation:
The NameNode tracks directories, file names, permissions, and block locations.
It does not normally store the actual user data blocks.
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Correct Answer: A. To merge the fsimage with accumulated edit-log changes
Explanation:
Checkpointing limits edit-log growth and speeds future NameNode recovery.
It does not replace block replication.
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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.
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Correct Answer: A. The degree of parallelism and the number of output part files
Explanation:
More reducers can process partitions concurrently but create more outputs and overhead.
The best count depends on data size, resources, and skew.
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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: B. DataNode
Explanation:
DataNodes store and serve HDFS blocks.
They also perform replication, deletion, and recovery as instructed.
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Correct Answer: B. Periodically creating namespace checkpoints
Explanation:
The Secondary NameNode merges the fsimage and edit log.
Its name is misleading because it was not simply an automatic backup NameNode.
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Correct Answer: B. The partitioner
Explanation:
The partitioner maps keys to reducer partitions.
A correct partitioner sends every identical key to the same reducer.
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Correct Answer: B. All intermediate keys are sent to a single reducer and final output can be globally sorted by key
Explanation:
One reducer receives all partitions, creating a global key order.
This can become a serious scalability bottleneck.
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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: C. The ability to handle increased load by adding resources
Explanation:
A scalable system maintains acceptable performance as data or users grow.
Distributed designs seek near-linear gains when nodes are added.
