Topic 1 Question 306
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 comply with data privacy requirements.
What should you do?
Use Dataflow and the Cloud Data Loss Prevention API to mask sensitive data. Write the processed 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 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 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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コメント(2)
- 正解だと思う選択肢: A
- Prioritizes Data Privacy: It protects sensitive information by masking it, reducing the risk of exposure in case of unauthorized access or accidental leaks.
- Reduces Data Sensitivity: Masking renders sensitive data unusable for attackers, even if they gain access to it.
- Preserves Data Utility: Masked data can still be used for consumer analyses, as patterns and relationships are often preserved, allowing meaningful insights to be derived.
👍 2raaad2024/01/06 - 正解だと思う選択肢: A
A. Use Dataflow and the Cloud Data Loss Prevention API to mask sensitive data. Write the processed data in BigQuery.
👍 1scaenruy2024/01/04
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