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Answer: Use Dataflow and the Cloud Data Loss Prevention API to mask sensitive data. Write the processed data in BigQuery.
The correct answer is A. Using Dataflow and the Cloud Data Loss Prevention (DLP) API to mask sensitive data ensures that data privacy requirements are met by masking sensitive information before it is written to BigQuery. This reduces the risk of exposure in case of unauthorized access or accidental leaks while preserving the utility of the data for analysis. Options B and D involve encryption but do not address masking or removing sensitive data, and option C suggests removal of sensitive fields entirely, which might not preserve the necessary data for analysis.
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As part of your role in preparing an organization-wide dataset for consumer analyses, you are tasked with preprocessing customer data. This data is stored in a restricted bucket within Google Cloud Storage. It is essential to ensure that during the preprocessing phase, all data privacy regulations are strictly adhered to, to protect sensitive customer information. What steps should you take to preprocess the data while ensuring compliance with data privacy requirements?
A
Use Dataflow and the Cloud Data Loss Prevention API to mask sensitive data. Write the processed data in BigQuery.
B
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.
C
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.
D
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.
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