Databricks-GenAI-Assoc Evaluation and Monitoring Practice Question
A data engineering team is deploying a RAG application using Mosaic AI Model Serving. They need to monitor the quality of the model's responses in production. Which Databricks feature should they use to capture and analyze inference data, such as requests, responses, and latency metrics?
⚠ Common exam trap
Candidates often confuse inference tables with MLflow experiments or standard Delta tables created manually. They forget that Mosaic AI Inference Tables are a native, built-in feature specifically for capturing model serving request and response logs.
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
✓
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.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Databricks SQL Alerts
Why it's wrong here
SQL Alerts are designed for monitoring table state changes or specific query results rather than capturing high-throughput inference payloads. They lack the native integration with Model Serving endpoints required to stream request-response pairs effectively into a structured format for large-scale LLM evaluation and performance observability.
- ✓
Mosaic AI Inference Tables
Why this is correct
Inference tables automatically log inference requests and responses to a Delta table in Unity Catalog. This enables seamless integration with monitoring tools for assessing model performance, latency, and throughput. It is the standardized method for capturing production data required to perform comprehensive model evaluation and drift detection.
- ✗
Delta Live Tables Audit Logs
Why it's wrong here
Delta Live Tables audit logs track pipeline execution status and lineage, not the internal payload of a served model. While they are useful for operational monitoring of ETL pipelines, they do not provide the granular request-response visibility necessary for evaluating generative AI model behavior and output quality.
- ✗
MLflow Experiment Tracking
Why it's wrong here
MLflow Experiment Tracking is primarily for logging metrics and parameters during the development and training phases of a model. It is not designed to capture live production inference data in real-time. Using it for production monitoring would require custom instrumentation that bypasses the native efficiency of inference tables.
About these practice questions
Courseiva writes every Databricks-GenAI-Assoc question from scratch — 330 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
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.