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Answer: Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention API. Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review.
**Explanation:** Option D is the correct answer because it provides: - **Real-time PII detection** using Cloud DLP API - **Automated data classification** with confidence levels - **Quarantine mechanism** for suspicious data - **Scalable cloud-native solution** using Cloud Functions - **Managed services** requiring minimal operational overhead Other options have limitations: - Option A: BigQuery authorized views only work after data is already stored, not during ingestion - Option B: Third-party tools on VMs require maintenance and don't leverage Google's managed services - Option C: Stackdriver logging is reactive and doesn't prevent PII exposure in real-time
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NO.6 You work for a shipping company that uses handheld scanners to read shipping labels. Your company has strict data privacy standards that require scanners to only transmit recipients' personally identifiable information (PII) to analytics systems, which violates user privacy rules. You want to quickly build a scalable solution using cloud-native managed services to prevent exposure of PII to the analytics systems. What should you do?
A
Create an authorized view in BigQuery to restrict access to tables with sensitive data.
B
Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive information.
C
Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may contain sensitive information.
D
Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention API. Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review.
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