Databricks Generative AI Engineer Associate exam dumps

Databricks Generative AI Engineer Associate practice question 244 of 306

Databricks Certified Generative AI Engineer Associate. Free level, Databricks. Free question with the correct answer and a full explanation.

Databricks Generative AI Engineer Associate Question 244

Select 3

You are tasked with deploying a generative AI model in a Databricks workspace for a financial institution. As part of the model governance process, you must ensure that the data used for training and inference complies with organizational and regulatory policies. Which of the following steps are critical to ensuring governance in this scenario?

  1. A

    Implement role-based access control (RBAC) to restrict access to sensitive data.

  2. B

    Enable automatic model versioning without tracking metadata.

  3. C

    Set up data lineage tracking to trace the origin and transformations of the training data.

  4. D

    Deploy the model without logging inference requests for privacy reasons.

  5. E

    Use a centralized feature store to ensure consistency and compliance in feature usage.

Show answer and explanation

Correct answers: A, C, E

Explanation

Governance in a generative AI project involves ensuring compliance with organizational policies and external regulations. Key actions include restricting access to sensitive data via RBAC, tracking data lineage to ensure transparency and regulatory compliance, and using a centralized feature store to maintain feature consistency and auditability. These steps collectively support secure, transparent, and compliant AI deployment.

  • A. Correct.

    Implementing role-based access control (RBAC) is critical to ensuring that only authorized users can access sensitive data, which aligns with governance and compliance requirements.

  • B. Incorrect.

    Automatic model versioning is useful, but without tracking metadata, it does not provide the necessary transparency and auditability for governance processes.

  • C. Correct.

    Data lineage tracking is essential for understanding the origin and transformations of the training data, ensuring compliance with data handling policies.

  • D. Incorrect.

    While logging inference requests should comply with privacy regulations, avoiding logging altogether may hinder auditability and monitoring, which are crucial for governance.

  • E. Correct.

    A centralized feature store ensures consistent feature usage and allows teams to track and audit feature compliance with organizational policies, aiding governance efforts.

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