Google Professional Cloud Security Engineer
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Your organization is developing a sophisticated machine learning (ML) model to predict customer behavior for targeted marketing campaigns. The BigQuery dataset used for training contains sensitive personal information. You need to design security controls for the AI/ML pipeline to maintain data privacy throughout the model's lifecycle, ensure personal data is not used for training, and restrict dataset access to only an authorized subset of individuals. 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.