You have data located in BigQuery that is used to generate reports for your company. You have noticed some weekly executive report fields do not correspond to format according to company standards…

Question

Options

• AUse Cloud Data Fusion and Wrangler to normalize the data, and set up a recurring job.
• BUse Dataflow SQL to create a job that normalizes the data, and that after the first run of the job, schedule the pipeline to execute recurrently.
• CCreate a Spark job and submit it to Dataproc Serverless.
• DUse BigQuery and GoogleSQL to normalize the data, and schedule recurring queries in BigQuery.

• A
  76% (22)
• B
  7% (2)
• C
  14% (4)
• D
  3% (1)

Explanation

Option A is correct because Cloud Data Fusion's Wrangler is a visual, no-code data preparation interface specifically designed for data cleansing and normalization tasks - like standardizing phone formats and country codes - without writing any code. Once you build the transformation pipeline visually, Data Fusion natively supports scheduling it as a recurring job, satisfying all three requirements: BigQuery integration, normalization, and recurrence with no coding.

Option B (Dataflow SQL) is wrong because writing SQL is coding, and scheduling a recurring Dataflow pipeline requires additional orchestration setup, making it neither "no coding" nor "quick."

Option C (Spark on Dataproc Serverless) is wrong because Spark jobs require writing code in Python, Scala, or Java - directly violating the "no coding" constraint.

Option D (BigQuery scheduled queries) is a tempting distractor since BigQuery does support scheduled queries and connects directly to your data, but writing GoogleSQL is still coding, and the question explicitly rules that out.

Memory tip: When a GCP exam question says "no coding" + "data normalization" + "recurring job," think Data Fusion + Wrangler - it's Google's purpose-built, code-free ETL tool with a visual wrangler for exactly these kinds of format-standardization tasks.

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