MCQ Collection
Big Data Analytics MCQs
Practice Big Data Analytics questions with answers and explanations.
Choose an option to check your answer.
A.
Providing only SQL parsing
B.
Managing HDFS block replication
C.
Replacing the driver process
D.
Connecting the application to the cluster and coordinating low-level Spark execution
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Correct Answer: D. Connecting the application to the cluster and coordinating low-level Spark execution
Explanation:
SparkContext is the main entry point for core RDD operations.
Choose an option to check your answer.
A.
They reduce data movement to zero
B.
They run only on the driver
C.
They eliminate partitions
D.
They require network transfer, serialization, sorting, and often disk I/O
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Correct Answer: D. They require network transfer, serialization, sorting, and often disk I/O
Explanation:
Shuffle data must be exchanged between executors.
Choose an option to check your answer.
A.
A mandatory SQL schema
B.
A method for joining only two RDDs
C.
A way to remove all keys
D.
A customizable framework for creating and merging per-key combiners
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Correct Answer: D. A customizable framework for creating and merging per-key combiners
Explanation:
combineByKey underlies many pair-RDD aggregations.
Choose an option to check your answer.
A.
Excessive scheduler overhead only
B.
Automatic data duplication
C.
Loss of RDD lineage
D.
Insufficient parallelism and oversized tasks
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Correct Answer: D. Insufficient parallelism and oversized tasks
Explanation:
A small number of partitions limits concurrent work.
Choose an option to check your answer.
A.
The optimizer has less visibility and may incur serialization overhead
B.
UDFs always run on the driver
C.
Built-in functions cannot use executors
D.
UDFs disable schemas entirely
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Correct Answer: A. The optimizer has less visibility and may incur serialization overhead
Explanation:
Built-in expressions participate fully in Catalyst optimization and code generation.
Choose an option to check your answer.
A.
A Spark engine for processing unbounded data using DataFrame and SQL semantics
B.
A file format for HDFS
C.
A YARN scheduling policy
D.
A Scala compiler mode
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Correct Answer: A. A Spark engine for processing unbounded data using DataFrame and SQL semantics
Explanation:
Structured Streaming treats a live stream conceptually as an ever-growing table.
Choose an option to check your answer.
A.
The unified entry point for Spark SQL, DataFrames, and related APIs
B.
A single executor task
C.
A YARN queue
D.
An HDFS edit log
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Correct Answer: A. The unified entry point for Spark SQL, DataFrames, and related APIs
Explanation:
SparkSession consolidates contexts used by higher-level Spark components.
Choose an option to check your answer.
A.
Produces one output element for each input element
B.
Produces zero or many outputs by flattening
C.
Groups all records by key
D.
Returns the RDD to the driver
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Correct Answer: A. Produces one output element for each input element
Explanation:
map applies a supplied function independently to every record.
Choose an option to check your answer.
A.
Transforms values while preserving keys and partitioning
B.
Transforms keys and discards values
C.
Groups all keys
D.
Moves data to the driver
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Correct Answer: A. Transforms values while preserving keys and partitioning
Explanation:
mapValues applies a function only to each value.
Choose an option to check your answer.
A.
High task scheduling overhead
B.
No parallel execution
C.
A single executor receives all data
D.
HDFS replication stops
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Correct Answer: A. High task scheduling overhead
Explanation:
Each task has launch and coordination costs.
Choose an option to check your answer.
A.
NULL equals every value
B.
It yields unknown rather than true, so null-specific functions are needed
C.
NULL is automatically converted to zero
D.
The query always fails
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Correct Answer: B. It yields unknown rather than true, so null-specific functions are needed
Explanation:
SQL uses three-valued logic for nulls.
Choose an option to check your answer.
A.
Processing the entire historical dataset once
B.
Processing incoming events in a sequence of small bounded batches
C.
Handling each event only on the driver
D.
Writing every event to a separate HDFS cluster
Show Answer
Correct Answer: B. Processing incoming events in a sequence of small bounded batches
Explanation:
Spark commonly groups recent records into short intervals.
