Databricks-ML-Pro ML Ops Practice Question
You are monitoring a model served on a Databricks Model Serving endpoint. You need to track the distribution of incoming request payloads to detect data drift. Which Databricks feature should you use to automatically capture and store inference logs for analysis?
⚠ Common exam trap
Many candidates confuse MLflow Tracking with inference logging; MLflow Tracking is for training runs, not for capturing live serving requests.
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
✓
Enable inference tables on the model serving endpoint.
Inference tables are a Databricks Model Serving feature that automatically logs request and response payloads to a Delta table. This enables easy querying and monitoring for data drift without writing additional code. Other options either do not capture payloads automatically or are not designed for this purpose.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable inference tables on the model serving endpoint.
Why this is correct
Inference tables in Databricks Model Serving automatically capture the request payloads, response payloads, and metadata for each request to the endpoint. These logs are stored in a Delta table that you can query for monitoring and drift detection. This feature is designed specifically for capturing inference data without additional code.
- ✗
Configure the endpoint to log to MLflow Tracking.
Why it's wrong here
MLflow Tracking is used for logging parameters, metrics, and artifacts during training runs, not for capturing real-time inference requests. It does not provide automatic logging of request payloads for served models. While you can log custom metrics from a serving endpoint, it requires manual instrumentation and does not capture raw payloads by default.
- ✗
Use Databricks SQL dashboards to query the endpoint's access logs.
Why it's wrong here
Databricks SQL dashboards can visualize data, but they do not automatically capture inference request payloads. Access logs may contain metadata about API calls but not the full request body. To analyze payload distribution, you need the actual data, which requires enabling a dedicated logging mechanism like inference tables.
- ✗
Enable model serving logs to be written to a cloud storage bucket.
Why it's wrong here
While model serving logs can be directed to cloud storage for debugging, they typically contain operational logs, not structured request payloads. This method does not provide a queryable table for drift analysis. Inference tables are the built-in feature for capturing request and response data in a Delta table, making them more suitable for monitoring.
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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.