Practice Library
All MCQs
Browse exam-wise, subject-wise, and country-wise MCQs with explanations.
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Correct Answer: A. The sequence of transformations used to derive an RDD
Explanation:
Lineage is a logical dependency graph.
Spark uses it for planning and fault recovery.
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Correct Answer: D. Spark delays transformations until an action is requested
Explanation:
Laziness allows Spark to optimize and pipeline operations.
Unused transformation chains do not perform work.
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Correct Answer: C. An operation that triggers execution and returns or writes a result
Explanation:
Actions include count, collect, reduce, and save operations.
They cause Spark to evaluate the necessary lineage.
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Correct Answer: B. A lazy operation that defines a new RDD
Explanation:
Transformations such as map and filter add steps to the lineage graph.
They are not executed until an action requires results.
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Correct Answer: A. Transformations create new RDDs rather than modifying existing ones
Explanation:
Immutability simplifies fault recovery and concurrent execution.
Lineage represents a functional chain of dataset versions.
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Correct Answer: D. Its partitions can reside and be processed across multiple executors
Explanation:
Partitioning enables parallel computation over cluster workers.
Each task normally handles one partition.
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Correct Answer: C. Lost partitions can be recomputed from lineage
Explanation:
Spark records how an RDD was derived from earlier datasets.
It reruns the required transformations after data loss.
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Correct Answer: B. Resilient Distributed Dataset
Explanation:
An RDD is an immutable distributed collection divided into partitions.
Its lineage allows failed partitions to be recomputed.
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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.
It provides methods for reading data and creating DataFrames.
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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.
Modern applications usually obtain it through SparkSession.
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Correct Answer: C. A worker process that runs tasks and stores cached data
Explanation:
Executors perform computation for one Spark application.
They also provide memory and disk storage for persisted partitions.
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Correct Answer: B. The process that runs the application logic and coordinates distributed execution
Explanation:
The driver builds execution plans, schedules jobs, and collects metadata.
It communicates with executors throughout the application.
