Databricks-ML-Assoc Model Deployment Practice Question
A team maintains a Databricks Model Serving endpoint for a fraud model. Compliance requires that every request and response be logged to a Delta table for auditing and later analysis. The endpoint is already configured and serving traffic. What should the team do to capture this data with the least additional infrastructure?
⚠ Common exam trap
It's easy for candidates to confuse operational endpoint logs and metrics with inference tables, which are the feature that actually captures request and response payloads.
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 endpoint so requests and responses are automatically logged to a Delta table
Inference tables are the native Model Serving mechanism that records request and response payloads into a Delta table, giving complete server-side auditing without extra infrastructure. Client-side logging, dashboard metrics, and event logs each capture only partial or operational data, so they cannot satisfy the requirement to log every request and response for the fraud endpoint.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Configure the endpoint to emit metrics to a monitoring dashboard and export the dashboard data nightly
Why it's wrong here
Metrics and dashboards aggregate performance statistics such as latency and error rates, not the raw request and response payloads. Exporting dashboard data cannot reconstruct individual prediction inputs and outputs, so it does not meet the compliance requirement for per-request auditing of the fraud model.
- ✗
Write a client wrapper that logs each request and response to a Delta table before returning the prediction
Why it's wrong here
A client wrapper only logs traffic that flows through that specific client and can be bypassed by other consumers of the endpoint. It also adds latency and code that must be maintained, and it does not capture server-side information such as the model version used. This fails to provide complete, reliable auditing for the endpoint.
- ✗
Attach an event log delivery to the serving endpoint and parse the logs into a Delta table with a scheduled job
Why it's wrong here
Serving endpoint event logs capture operational events like configuration changes and scaling activity, not the inference request and response payloads. Parsing them would not yield the prediction data needed for auditing, and it adds a scheduled job, so it is both incorrect and more complex than the built-in logging feature.
- ✓
Enable inference tables on the endpoint so requests and responses are automatically logged to a Delta table
Why this is correct
Inference tables are a built-in Model Serving feature that automatically logs the request payloads and model responses to a Delta table in Unity Catalog. Enabling them requires no extra pipelines or clusters and directly satisfies the auditing requirement, making it the least-infrastructure solution for capturing endpoint traffic.
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-Assoc 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-Assoc exam.