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GCP Professional Data Engineer Practice Question

The marketing analytics team stores semi-structured CSV files in Cloud Storage. Business analysts-who do not write code-must interactively profile, deduplicate, and standardize the files, preview every step, then operationalize the recipe so it runs nightly and lands cleansed data in BigQuery. You want minimal operational overhead and no cluster management. Which Google Cloud service best meets these requirements?

  • Create a Cloud Composer DAG that calls a BigQuery stored procedure containing the required cleansing SQL.

  • Deploy a persistent Dataproc cluster and trigger a nightly PySpark job with Cloud Scheduler to cleanse and load the data.

  • Build a Cloud Data Fusion pipeline that launches an ephemeral Dataproc cluster each night to transform and load the data.

  • Use Cloud Dataprep to build a visual recipe and schedule it; the service will execute the steps as a serverless Dataflow job that writes to BigQuery.

GCP Professional Data Engineer
Ingesting and processing the data
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