Databricks-GenAI-Assoc Evaluation and Monitoring Practice Question
A team has deployed a RAG chatbot on Databricks and enabled inference table logging. They want to set up automated monitoring to detect when the average response length increases significantly compared to the baseline. Which Databricks feature should they use to create this monitor?
⚠ Common exam trap
Candidates often confuse batch evaluation tools like `mlflow.evaluate()` with continuous monitoring features like Lakehouse Monitoring, which are purpose-built for tracking drift in production tables.
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
✓
Lakehouse Monitoring for the inference table, with a custom metric for response length.
Lakehouse Monitoring is designed to monitor Delta tables, including inference tables, for data quality and drift. By creating a monitor with a custom metric for response length, the team can automatically detect significant changes from the baseline and receive alerts. This is the most direct and integrated approach on Databricks for this monitoring requirement.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Mosaic AI Model Serving endpoint logs analyzed with the `mlflow.evaluate()` API on a schedule.
Why it's wrong here
`mlflow.evaluate()` is for batch evaluation of models against a dataset, not for continuous monitoring of production traffic. It does not automatically analyze serving endpoint logs or compute drift. Using it on a schedule would require exporting logs and writing custom evaluation logic. This is inefficient and does not leverage Databricks' monitoring capabilities.
- ✗
Databricks SQL alerts on a scheduled query that calculates the average response length from the inference table.
Why it's wrong here
SQL alerts can detect threshold breaches on a schedule, but they require manual query setup and do not provide built-in drift detection or baseline comparison. Lakehouse Monitoring automatically computes profiles and drift metrics. While SQL alerts are flexible, they lack the integrated monitoring framework and would require more maintenance. They are not the recommended feature for this scenario.
- ✓
Lakehouse Monitoring for the inference table, with a custom metric for response length.
Why this is correct
Lakehouse Monitoring can track statistical properties of Delta tables, including inference tables. By defining a custom metric for response length, the team can monitor drift and receive alerts when the average deviates from the baseline. This is the native Databricks solution for monitoring data quality and model performance over time. It integrates with the inference table without additional ETL.
- ✗
MLflow Model Registry webhooks to trigger a retraining pipeline when response length changes.
Why it's wrong here
MLflow Model Registry webhooks are for model lifecycle events like version transitions, not for monitoring data drift in inference tables. They cannot compute statistics or detect changes in response length. Using them here would require custom code and would not provide the automated monitoring needed. They are the wrong tool for this observability task.
About these practice questions
This Databricks-GenAI-Assoc question is part of Courseiva's 330-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam 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.