You receive data files in CSV format monthly from a third party. You need to cleanse this data, but every third month the schema of the files changes. Your requirements for implementing these…

Question

Options

• AUse Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis
• BLoad each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard schema.
• CHelp the analysts write a Cloud Dataflow pipeline in Python to perform the transformation.
• DUse Apache Spark on Cloud Dataproc to infer the schema of the CSV file before creating a Dataframe.

• A
  81% (29)
• B
  11% (4)
• C
  3% (1)
• D
  6% (2)

Explanation

Cloud Dataprep is the right choice because it's a managed, serverless service purpose-built for visually preparing and transforming data - it provides a graphical interface for building "transformation recipes," supports scheduled execution, and is designed for non-developer users like analysts without requiring any coding.

Option B is wrong because writing SQL in BigQuery requires developer/SQL skills and provides no graphical design tool for transformations. Option C fails because writing a Dataflow pipeline in Python explicitly requires developer expertise, directly contradicting the "non-developer analysts" requirement. Option D is wrong because Cloud Dataproc/Spark is a general-purpose big data processing engine - it has no built-in graphical transformation designer and still requires engineering skill to use.

Memory tip: Think of Dataprep as "Excel for cloud data pipelines" - it's the only GCP tool in this list that combines a Graphical interface, Scheduling, and Self-service for analysts (no code). Whenever an exam question mentions analysts + visual + scheduled transformations together, that triple signal points directly to Dataprep.

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