H13-723_V2.0 · Question #6
As the core object of Spark, which of the following characteristics does RDD have? (multiple choice)
The correct answer is A. Read only B. Partition C. Fault tolerance D. Efficient. All four options are correct because RDD (Resilient Distributed Dataset) is defined by these fundamental properties. A (Read-only/Immutable): RDDs cannot be modified after creation - transformations always produce a new RDD, preventing data corruption in concurrent…
Question
As the core object of Spark, which of the following characteristics does RDD have? (multiple choice)
Options
- ARead only
- BPartition
- CFault tolerance
- DEfficient
How the community answered
(64 responses)- A100% (64)
Explanation
All four options are correct because RDD (Resilient Distributed Dataset) is defined by these fundamental properties. A (Read-only/Immutable): RDDs cannot be modified after creation - transformations always produce a new RDD, preventing data corruption in concurrent environments. B (Partitioned): RDDs are split into partitions distributed across cluster nodes, enabling parallel computation. C (Fault-tolerant): The "Resilient" in RDD means Spark tracks each RDD's lineage (the chain of transformations), so lost partitions can be recomputed automatically without storing redundant copies. D (Efficient): RDDs use lazy evaluation (transformations don't execute until an action is triggered) and in-memory caching, minimizing disk I/O and recomputation.
Since every option is correct here, there are no distractors to eliminate - the trap is assuming one of these traits doesn't apply.
Memory tip: Use the acronym RIPE - Read-only, Immutable partitions, Partitioned across nodes, Efficient via lineage - or just remember the word "Resilient" in RDD encapsulates fault tolerance, while "Distributed" implies partitioning, and "Dataset" implies read-only, structured data.
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