How to Protect Sensitive Customer Data While Retaining All Data?

PDE By islever Editorial Last updated Aug 31, 2026

Short answer

C Use Dataflow and the Cloud Data Loss Prevention API to mask sensitive data. Write the processed data in BigQuery.

Question

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?

Use the Cloud Data Loss Prevention API and Dataflow to detect and remove sensitive fields from the data in Cloud Storage. Write the filtered data in BigQuery.
Use customer-managed encryption keys (CMEK) to directly encrypt the data in Cloud Storage. Use federated queries from BigQuery. Share the encryption key by following the principle of least privilege.
Use Dataflow and the Cloud Data Loss Prevention API to mask sensitive data. Write the processed data in BigQuery. Correct Answer
Use Dataflow and Cloud KMS to encrypt sensitive fields and write the encrypted data in BigQuery. Share the encryption key by following the principle of least privilege.

Key Takeaways

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.

Common mistake: 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.

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

https://cloud.google.com/dataflow/docs

This link opens the official vendor documentation in a new tab.

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.

Community Votes

100%

100% of anonymous learners picked answer C. Votes are pick records left by other test-takers — they are not the verified answer.

Discussion 3 contributions

Compiled from public community threads and reader submissions, lightly edited for clarity. Handles and vote tallies are shown as recorded, and comments reflect the views of individual contributors.

From public community threads 3

HectorLeon2099 👍 6 Answered C

It's C. "A" removes data and retaining all is a requirement.
Pime13 👍 3 Answered C

C. Use Dataflow and the Cloud Data Loss Prevention API to mask sensitive data. Write the processed data in BigQuery. This approach ensures that sensitive data elements are protected through masking, which meets data privacy requirements. At the same time, it retains the data in a usable form for future analyses
Nagamanikanta 👍 1 Answered C

option C we can simply mask the data and process in biguery

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