Databricks-GenAI-Assoc Evaluation and Monitoring Practice Question
A team has deployed a RAG application on Databricks and wants to monitor the quality of responses in production. They have enabled inference table logging. Which built-in Databricks capability allows them to periodically evaluate the logged requests and responses for quality metrics like groundedness?
⚠ Common exam trap
The trap here is assuming that any monitoring tool, like SQL dashboards or Delta Live Tables expectations, can compute semantic quality metrics, when only Agent Evaluation provides LLM-specific judges.
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 Agent Evaluation, which can be run on the inference table to compute quality metrics.
Mosaic AI Agent Evaluation is the built-in Databricks capability for evaluating RAG and agent applications. It can process inference tables and compute quality metrics such as groundedness, relevance, and answer correctness. This enables periodic, automated monitoring of production quality without custom code. It is the correct tool for the described scenario.
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 dashboards that display the count of requests per hour.
Why it's wrong here
SQL dashboards can visualize request volume but do not compute quality metrics like groundedness. They are useful for operational monitoring but lack the semantic evaluation needed for RAG quality. The team needs to assess the content of responses, not just counts. Therefore, SQL dashboards alone are insufficient.
- ✗
Delta Live Tables expectations that validate response length.
Why it's wrong here
Delta Live Tables expectations are for data quality checks on streaming or batch pipelines, such as schema validation or null checks. They cannot compute semantic quality metrics like groundedness. Using them for this purpose would be a misuse of the feature. They do not provide LLM-specific evaluation.
- ✓
Mosaic AI Agent Evaluation, which can be run on the inference table to compute quality metrics.
Why this is correct
Mosaic AI Agent Evaluation is designed to evaluate agent and RAG applications. It can be run on logged inference tables to compute metrics such as groundedness and relevance. This allows continuous monitoring of production quality without manual intervention. It integrates with MLflow and Databricks workflows, making it the appropriate built-in capability for this scenario.
- ✗
MLflow Tracking with custom metrics logged during model serving.
Why it's wrong here
MLflow Tracking logs metrics and parameters during experiments, but it does not automatically evaluate inference tables for quality. Custom metrics would need to be computed and logged by the application code, which is not built-in. This approach requires significant engineering effort and is not a turnkey monitoring solution. It is not the built-in capability referenced.
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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.