Professional Cloud Security EngineerDemo

By Google · Question Mode

QuestionQ13

Ensuring data protection

Your organization is building a sophisticated machine learning (ML) model that predicts customer behavior for targeted marketing campaigns. The BigQuery dataset used for training contains sensitive personal information. You must design security controls for the AI/ML pipeline. Data privacy must be preserved throughout the model’s lifecycle, and personal data must not be used during training. You must also limit dataset access to only an authorized subset of people. What should you do?

  • A De-identify sensitive data before model training by using Cloud Data Loss Prevention (DLP)APIs. and implement strict Identity and Access Management (IAM) policies to control access to BigQuery.
  • B Implement Identity-Aware Proxy to enforce context-aware access to BigQuery and models based on user identity and device.
  • C Implement at-rest encryption by using customer-managed encryption keys (CMEK) for the pipeline. Implement strict Identity and Access Management (IAM) policies to control access to BigQuery.
  • D Deploy the model on Confidential VMs for enhanced protection of data and code while in use. Implement strict Identity and Access Management (IAM) policies to control access to BigQuery.
Explanation

De-identifying sensitive fields before training prevents personal data from being used by the model while retaining usable data for analysis. Cloud DLP, now part of Sensitive Data Protection, supports discovery and de-identification of sensitive BigQuery data. BigQuery IAM enables granular, least-privilege access so only authorized users can access the dataset.

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