Associate Data Practitioner · Free Practice Question Easy
Question 24
Which ETL tool would be most appropriate for transforming data using Apache Spark without managing infrastructure?
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A
Dataflow
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B
Dataproc Serverless
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C
Cloud Data Fusion
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D
BigQuery
Reveal correct answer
Correct answer: B
Explanation
Option B is CORRECT. Dataproc Serverless allows you to run Apache Spark jobs without managing any infrastructure, making it the most appropriate choice for Spark-based transformations with minimal operational overhead. With Dataproc Serverless, you submit Spark batch workloads and Google Cloud automatically provisions and manages the Spark environment, handling scaling, dependencies, and infrastructure. You only pay for the resources used during job execution.
Option A is INCORRECT. Dataflow uses Apache Beam, not Spark, so it's not suitable for existing Spark code. While Dataflow is also serverless, it requires code written in the Beam programming model, which would mean rewriting existing Spark transformations.
Option C is INCORRECT. Cloud Data Fusion provides a visual interface for ETL but uses Dataproc clusters behind the scenes, which still requires some cluster configuration. It's not optimized for running custom Spark code without modifications.
Option D is INCORRECT. BigQuery uses SQL for transformations, not Apache Spark, so existing Spark code couldn't be directly utilized. While BigQuery is serverless, it doesn't support the Spark programming model or APIs.
Discussion
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