Databricks-ML-Pro Model Deployment Practice Question
A team has deployed a model to a Databricks Model Serving endpoint. They want to monitor the endpoint's performance and detect data drift over time. Which Databricks feature should they use to automatically track inference data and compute drift metrics?
⚠ Common exam trap
It's easy for candidates to confuse operational logs with inference data capture; endpoint logs do not contain the payloads needed for drift detection.
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
✓
Inference tables
Inference tables are a Databricks feature that automatically logs the input and output of a model serving endpoint to a Delta table. This data can then be used to compute drift metrics, monitor model performance, and trigger alerts. It is the built-in solution for capturing inference data for monitoring purposes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Inference tables
Why this is correct
Inference tables automatically capture the request and response payloads for a serving endpoint and store them in a Delta table. This allows you to monitor model performance, detect data drift, and analyze predictions over time using Databricks SQL or notebooks.
- ✗
MLflow tracking server
Why it's wrong here
MLflow tracking is used for logging parameters, metrics, and artifacts during model training. It does not automatically capture inference data from a serving endpoint. While you can log metrics manually, it is not designed for real-time monitoring of deployed models.
- ✗
Model serving endpoint logs
Why it's wrong here
Endpoint logs provide information about the operational health of the endpoint, such as errors and latency, but they do not capture the actual request and response payloads. To monitor data drift, you need the input features and predictions, which are not available in standard logs.
- ✗
Databricks SQL dashboards
Why it's wrong here
Databricks SQL dashboards can visualize data, but they do not automatically collect inference data. You would need to set up a data pipeline to capture and store inference logs first. Without inference tables, there is no built-in mechanism to populate the dashboard with live inference data.
About these practice questions
Courseiva writes every Databricks-ML-Pro question from scratch — 300 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-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.