Databricks-ML-Pro Model Deployment Practice Question
A fraud detection team has a model registered in Unity Catalog as main.ml.fraud_model. They need to serve it in real time, but compliance requires that every scoring request automatically generate an audit record in a Delta table, and that the model only be promoted to production after a human reviews the audit logs from a canary period. Which deployment configuration satisfies these requirements?
⚠ Common exam trap
The trap here is assuming that enabling model monitoring also captures raw request and response payloads, when monitoring only derives metrics from the Inference Table that must be enabled separately.
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
✓
Deploy the model with Databricks Model Serving and enable Inference Tables, then use the endpoint's request/response logs to drive the review before promoting the model version via a UC alias.
Inference Tables are the Databricks mechanism that automatically logs the request and response payloads of a served model into a Delta table, which directly produces the compliance audit trail. Pairing that with a Unity Catalog alias lets the team hold production traffic on the prior version and repoint the alias only after reviewing the canary logs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Deploy the model with Databricks Model Serving and configure the endpoint to write request logs to a mounted cloud storage path using a cluster-scoped log4j appender.
Why it's wrong here
Model Serving endpoints run as serverless managed compute and do not expose a cluster-scoped logging configuration that can mount cloud storage or attach a log4j appender. The supported mechanism for capturing request and response payloads is Inference Tables, which writes to a Delta table in Unity Catalog rather than an arbitrary mounted path.
- ✓
Deploy the model with Databricks Model Serving and enable Inference Tables, then use the endpoint's request/response logs to drive the review before promoting the model version via a UC alias.
Why this is correct
Inference Tables capture the payloads and responses of every scored request into a Delta table automatically, which is exactly the audit record compliance needs. Because the endpoint references a UC model version through an alias, the team can point the production alias at the canary version only after reviewing those logs, giving a clean, reversible promotion path.
- ✗
Deploy the model behind a Databricks SQL warehouse by registering it as a Python UDF, and rely on the warehouse's query history as the audit trail.
Why it's wrong here
A SQL warehouse executes batch or interactive SQL, not low-latency real-time HTTP scoring, so it does not meet the real-time serving requirement. Query history also records SQL statements rather than raw feature payloads and model responses, so it would not give the compliance team the request-level audit records they asked for.
- ✗
Deploy the model with Databricks Model Serving and enable model monitoring, which persists every raw request and response to a Delta table for human review.
Why it's wrong here
Model monitoring in Databricks computes statistical metrics such as data drift and profile statistics from Inference Table data; it does not itself persist raw request and response payloads. You must enable Inference Tables separately to capture the payloads, so monitoring alone cannot satisfy the audit-record requirement.
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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.