Google Professional Data Engineer Question 11
Single answerGoogle Cloud PlatformYou are designing a data pipeline for a healthcare organization that processes patient information. The pipeline ingests data into Google Cloud Storage and processes it using Dataflow before storing it in BigQuery. To comply with data privacy regulations, you need to identify and mask sensitive data such as Social Security numbers and credit card information before storing the data in BigQuery. Which approach should you take?
- A
Use Cloud Data Loss Prevention (Cloud DLP) API to inspect and de-identify sensitive data in the pipeline before storing it in BigQuery.
- B
Create custom scripts in Dataflow to manually identify and mask sensitive data based on predefined patterns.
- C
Store the raw data in BigQuery and use SQL queries to identify and mask sensitive data after ingestion.
- D
Use BigQuery's native encryption feature to automatically mask sensitive data during storage.
Show answer and explanation
Correct answer: A
Explanation
The Cloud Data Loss Prevention (Cloud DLP) API is the most suitable tool for identifying and de-identifying sensitive data such as Social Security numbers and credit card information. It enables data masking or tokenization during the pipeline process, ensuring compliance with data privacy regulations before storing the data in BigQuery. This ensures that sensitive information is protected throughout the data processing lifecycle.
- A. Correct.
This is the correct approach because Cloud DLP is specifically designed to identify and de-identify sensitive information such as PII (Personally Identifiable Information) and PCI (Payment Card Industry) data, ensuring compliance with privacy regulations before storing the data in BigQuery.
- B. Incorrect.
While it is possible to create custom scripts for identifying and masking sensitive data, this approach is error-prone, requires significant development effort, and lacks the robustness and flexibility of Cloud DLP.
- C. Incorrect.
Identifying and masking sensitive data after storing it in BigQuery violates data privacy regulations, as sensitive data is already exposed during the ingestion process.
- D. Incorrect.
BigQuery's native encryption ensures data is encrypted at rest but does not provide functionality to mask or de-identify sensitive data automatically.