You have uploaded 5 years of log data to Cloud Storage. A user reported that some data points in the log data are outside of their expected ranges, which indicates errors. You need to address this…

Question

Options

• AImport the data from Cloud Storage into BigQuery. Create a new BigQuery table, and skip the rows with errors.
• BCreate a Compute Engine instance and create a new copy of the data in Cloud Storage Skip the rows with errors
• CCreate a Cloud Dataflow workflow that reads the data from Cloud Storage, checks for values outside the expected range, sets the value to an appropriate
• DCreate a Cloud Dataflow workflow that reads the data from Cloud Storage, checks for values outside the expected range, sets the value to an appropriate

Explanation

Note: Options C and D appear truncated in the question - both describe a Cloud Dataflow workflow that corrects out-of-range values. Based on standard GCP exam context, C or D (the Dataflow approach) is correct.

A Cloud Dataflow pipeline is the right tool here because it provides a repeatable, scalable ETL pipeline - you define the workflow once and run it again anytime. Critically, it reads from Cloud Storage without modifying the original files, satisfying the compliance requirement to preserve raw data, while writing corrected output to a separate destination. Options A and B both "skip rows with errors" rather than correcting them, which means you lose those data points entirely - not an acceptable data quality fix. Option B (Compute Engine) is also a manual, ad-hoc approach with no built-in repeatability or scalability. Option A (BigQuery import + skip) similarly discards erroneous records rather than remediating them.

Memory tip: When an exam question mentions compliance (keep originals) + repeatable process + data correction (not deletion), think Dataflow - it's Google's managed pipeline service designed for exactly this pattern: read source, transform, write to a new sink, leaving the original untouched.

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