Databricks-ML-Pro Model Deployment Practice Question
An ML engineer has deployed a model to Databricks Model Serving and wants to monitor the endpoint's performance over time. They need to track the number of requests, latency, and error rates. Which Databricks feature provides these metrics out-of-the-box?
⚠ Common exam trap
The trap here is assuming MLflow Tracking, which is used during training, also monitors deployed endpoints, when in fact Model Serving has its own metrics dashboard.
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
✓
Databricks Model Serving endpoint metrics in the Databricks UI
Databricks Model Serving includes built-in metrics that are accessible in the Databricks UI. These metrics cover request counts, latency, and error rates, providing immediate visibility into endpoint performance. Other options like MLflow Tracking or Unity Catalog audit logs serve different purposes and do not offer out-of-the-box endpoint monitoring.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Delta Live Tables
Why it's wrong here
Delta Live Tables is a framework for building reliable data pipelines. It is not designed for monitoring model serving endpoints. While you could build a pipeline to process logs, it is not an out-of-the-box solution for endpoint metrics.
- ✓
Databricks Model Serving endpoint metrics in the Databricks UI
Why this is correct
Databricks Model Serving provides built-in metrics such as request count, latency, and error rates, which are displayed in the endpoint's detail page in the Databricks UI. These metrics are available without additional configuration and can be used to monitor the health and performance of the endpoint.
- ✗
Unity Catalog audit logs
Why it's wrong here
Unity Catalog audit logs record access and governance events, such as who accessed a table or model. They do not provide performance metrics like request latency or error rates for model serving endpoints. They are useful for security auditing, not operational monitoring.
- ✗
MLflow Tracking
Why it's wrong here
MLflow Tracking is used to log parameters, metrics, and artifacts during model training and experimentation. It does not provide real-time monitoring of deployed model serving endpoints. While you can log custom metrics from a model, it is not an out-of-the-box monitoring solution for endpoint performance.
About these practice questions
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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-ML-Pro 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-ML-Pro exam.