Databricks-GenAI-Assoc Flashcards — Free Databricks Certified Generative AI Engineer Associate Study Cards

Utilize Unity Catalog column-level masking functions.

Unity Catalog's dynamic views and column-level masking policies are the standard for securing PII. By applying functions like MASK or current_user() within a view definition, administrators decouple security logic from physical table storage. This approach is essential for compliance, ensuring that sensitive data is hidden at query time without duplicating datasets, thus maintaining a single source of truth while enforcing granular access control across all Databricks workspaces and compute resources.

Configure the 'inference_table_config' block in the endpoint request JSON.

Enabling 'inference_table_config' in the model serving endpoint configuration is the standard Databricks approach for capturing telemetry data. This feature automatically writes request and response payloads to a Delta table, enabling compliance auditing, model monitoring, and drift detection. Understanding this integration is critical for production-grade AI deployments where transparency, debugging, and regulatory logging are mandatory requirements for enterprise-scale machine learning operations.

MLflow Model Registry

MLflow Model Registry is the central feature for managing the lifecycle of models, including versioning and stage transitions. Understanding how to promote a model from 'Staging' to 'Production' is fundamental for ensuring that only tested and verified models reach the end-users. This workflow is critical for maintaining quality and stability in generative AI applications, as it provides a structured process for model evolution and deployment management.

Mosaic AI Inference Tables

Mosaic AI Model Serving provides built-in inference tables to automatically capture request and response logs. By enabling these tables, engineers can export data to a Unity Catalog table for analysis. This is critical for monitoring performance, data drift, and model quality over time. Without this feature, teams lack the visibility required for production-grade LLM governance and continuous improvement cycles within the Databricks ecosystem.

Dynamic data masking

Unity Catalog dynamic data masking allows administrators to apply functions to columns that redact or obfuscate data based on the user's role or group membership. By using SQL functions like mask_email or custom UDFs within a masking policy, the data remains consistent at the physical layer while presenting transformed values at query time. This ensures compliance with privacy regulations without creating multiple copies of datasets.

Deploy an embedding model on Mosaic AI Model Serving and utilize Databricks Vector Search.

Utilizing Mosaic AI Model Serving with private endpoints and leveraging Vector Search indexes ensures that both the embedding model and the retrieval process occur within the Databricks control plane. By avoiding external API calls to third-party providers, the organization maintains strict governance, data residency compliance, and lower latency for inference, which is critical for enterprise-grade generative AI applications handling sensitive corporate documents.