Databricks-ML-Assoc Model Deployment Practice Question
A team wants to monitor a production Model Serving endpoint for data drift and to capture the exact request payloads and responses for later auditing. They want this logging to happen automatically without adding code to the client application. What should they configure on the endpoint?
⚠ Common exam trap
The trap here is assuming that operational logs or MLflow tracking can substitute for inference tables, when only inference tables capture structured request and response payloads at the endpoint.
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 for monitoring and auditing.
Inference tables are the native Model Serving feature that logs request and response payloads to a Delta table automatically, enabling drift monitoring and auditing without touching client code. Client middleware, container logs, and MLflow experiments either require application changes, lack payload structure, or are not built for high-volume inference logging.
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 endpoint so requests and responses are automatically logged to a Delta table for monitoring and auditing.
Why this is correct
Inference tables capture the request and response payloads of a serving endpoint into a Delta table automatically, with no client changes. Teams can then join those logs against training data to compute drift metrics and retain an audit trail. Enabling the feature is a configuration step on the endpoint, matching the requirement for automatic, code-free logging.
- ✗
Attach an MLflow experiment to the endpoint so each prediction is recorded as a run with parameters and metrics.
Why it's wrong here
MLflow experiments and runs are designed for tracking training and evaluation, not for logging high-volume inference traffic. Recording every prediction as a run would be inefficient and would not produce a queryable table of requests and responses. This misapplies the experiment concept to a serving concern that inference tables are built to handle.
- ✗
Add client-side logging middleware in the application that writes each request and response to a Delta table before and after calling the endpoint.
Why it's wrong here
Client-side middleware requires modifying the calling application and maintaining logging code, which contradicts the goal of automatic logging without client changes. It also centralizes logs only for that client, so other consumers of the endpoint are invisible. Inference tables exist precisely to avoid this duplication and to capture all traffic at the endpoint.
- ✗
Configure the endpoint to write its container stdout and stderr to a log delivery location in cloud storage.
Why it's wrong here
Container logs capture operational messages, not structured request and response payloads, so they cannot support drift analysis or precise auditing of inputs and outputs. They also lack the schema needed to join with training features. Log delivery is useful for debugging, but it does not provide the payload-level capture that inference tables deliver.
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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.