Databricks-GenAI-Assoc Application Development Practice Question
A developer is building a RAG application using Mosaic AI Model Serving. They need to ensure that the model endpoint logs inference requests and responses for audit purposes. Which configuration parameter should they enable?
⚠ Common exam trap
Candidates often assume logging is automatic or handled by the workspace. You must explicitly configure the 'inference_table_config' to capture request/response data into a Delta table for auditing.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
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.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable 'auto_capture_logs' in the endpoint environment variables.
Why it's wrong here
Environment variables are intended for runtime configuration like API keys or database connection strings, not for defining infrastructure-level telemetry sinks. Using environment variables for logging configuration would bypass the native Databricks Model Serving audit framework, failing to integrate with Delta tables for scalable data analysis.
- ✓
Configure the 'inference_table_config' block in the endpoint request JSON.
Why this is correct
The 'inference_table_config' parameter is the specific configuration key required to enable automated request and response logging in Databricks Model Serving. This maps incoming requests directly to a specified Delta table, providing a structured, queryable record of all model interactions, which is essential for ongoing performance monitoring.
- ✗
Set the 'log_level' to 'DEBUG' in the Serving Endpoint settings.
Why it's wrong here
Log levels primarily control the verbosity of system and internal application logs, not the persistence of inference payloads. Setting log levels to DEBUG will simply flood your driver logs with internal framework messages, failing to provide the structured storage required for audit trails and model performance metrics.
- ✗
Enable 'external_logging' in the Unity Catalog schema settings.
Why it's wrong here
Unity Catalog governs data access, governance, and lineage, but it does not provide an 'external_logging' feature for capturing model inference payloads. Inference tables must be configured at the serving endpoint level, as they represent a functional integration between the serving infrastructure and the storage layer.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
This Databricks-GenAI-Assoc practice question is part of Courseiva's free Databricks certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Databricks-GenAI-Assoc exam.