How to Protect Sensitive Customer Data While Retaining All Data?
You are preparing an organization-wide dataset. You need to preprocess customer data stored in a restricted bucket in Cloud Storage. The data will be used to create consumer analyses. You need to follow data privacy requirements, including protecting certain sensitive data elements, while also retaining all of the data for potential future use cases. What should you do?
Community Votes
100% of anonymous learners picked answer C. Votes are pick records left by other test-takers — they are not the verified answer.
Community Insight
The exam tests selecting the correct de-identification technique, and the trap is choosing 'remove sensitive fields' (Option A), which violates the explicit requirement to retain all data for future use cases.
Using Dataflow with the Cloud Data Loss Prevention (DLP) API to mask sensitive fields de-identifies customer data for consumer analyses while preserving every record in BigQuery. The community unanimously (100%) votes for option C because masking protects privacy without discarding data.
Option A is the most tempting wrong answer because DLP detection plus Dataflow sounds like the right pipeline, but 'remove sensitive fields' permanently deletes data, breaching the retention requirement stated in the question.
Community Discussion (3 comments)
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Expert Analysis
Why the Answer Is Correct
Option C uses Dataflow with the Cloud DLP API to mask sensitive data elements, satisfying the privacy requirement while keeping every field and record intact for potential future use cases. Masking transforms sensitive values (e.g., redaction or tokenization) but preserves the dataset structure, so consumer analyses in BigQuery remain valid and useful. Writing the processed output to BigQuery creates a governed, analytics-ready dataset that downstream teams can query safely. This mirrors Google's recommended pattern of de-identifying data at scale with DLP before loading it into an analytics warehouse.Why the Other Options Are Wrong
Option A detects and removes sensitive fields, which permanently discards data and directly conflicts with the requirement to retain all data for future use cases, as the top-voted comment explains. Option B applies CMEK encryption to the whole bucket, but encryption does not de-identify anything: anyone granted the key sees raw sensitive values, and federated queries add complexity without solving the privacy problem. Option D encrypts sensitive fields with Cloud KMS, but encrypted fields are unreadable for consumer analyses unless keys are shared, which undermines protection; encryption is a security control, not a de-identification technique. None of A, B, or D can both protect sensitive elements and keep the data fully retained and usable.Community Comment Notes
Voting is 100% for C with no dissent, showing strong consensus. The most-liked comment (6 likes) states plainly that A fails because removing data violates the retention requirement. Another comment reinforces that masking meets data privacy requirements while keeping the data in a usable form for future analyses, and a third confirms that masking plus BigQuery is the straightforward path. No commenters defended B or D, confirming the community view that masking is the only technique satisfying both constraints.Official Reference
Exam Strategy
When a question states that all data must be retained for future use, immediately eliminate any option that removes, filters, or deletes fields. Distinguish privacy techniques: masking or tokenization de-identifies data while keeping it usable for analytics, whereas encryption protects data but leaves it unreadable without key access. Watch the verbs in the options — 'mask' beats 'remove' whenever retention is a stated requirement.