Databricks-ML-Pro ML Ops Practice Question
A fraud-detection model is registered in Unity Catalog and deployed to a Model Serving endpoint. Compliance requires that every prediction be traceable to the exact model version and the request that produced it. Which combination of Databricks features should you configure to meet this requirement?
⚠ Common exam trap
The trap here is assuming that MLflow autologging or a model signature provides runtime prediction traceability, when those features operate at training time or schema validation only.
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 table logging on the endpoint and register the model version in Unity Catalog with a descriptive comment.
Traceability of individual predictions requires server-side capture of the request, response, and serving model version, which is what inference tables provide, combined with Unity Catalog's governed model version identity. Training-time logging, endpoint configuration snapshots, and client-side logs each capture only part of the picture and cannot reconstruct the full per-prediction audit trail.
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 table logging on the endpoint and register the model version in Unity Catalog with a descriptive comment.
Why this is correct
Inference tables record each request, response, timestamp, and the served model version, while Unity Catalog provides the lineage and version identity of the registered model. Together they let an auditor trace any prediction back to the exact model version and the originating request, which is precisely the compliance requirement.
- ✗
Attach a model signature to the registered model and enable request logging in the serving client application.
Why it's wrong here
A model signature validates input and output schemas, and client-side logging depends on the calling application being instrumented and trusted. Client logs are not authoritative for the platform and can be lost or altered, so they fail to provide the reliable, server-side traceability that compliance demands.
- ✗
Configure the endpoint with a scale-to-zero policy and log the endpoint configuration to a Delta table daily.
Why it's wrong here
Scale-to-zero affects cost and cold-start latency, and a daily configuration snapshot only shows what the endpoint looked like at snapshot time. It does not capture individual prediction requests or bind them to the model version that served them, so the per-prediction audit trail is missing.
- ✗
Enable MLflow autologging in the training notebook and set the model version stage to Production.
Why it's wrong here
Autologging captures training-time parameters, metrics, and artifacts, and stage transitions are legacy registry metadata. Neither records runtime prediction requests or links a served response to a specific model version at inference time, so the traceability requirement for individual predictions is not satisfied.
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.