Databricks-ML-Assoc Model Deployment Practice Question
A machine learning engineer needs to capture every request and response payload sent to a Databricks Model Serving endpoint so that the team can later join predictions with ground-truth labels for monitoring. Which Databricks feature should they enable on the endpoint?
⚠ Common exam trap
The trap here is assuming that enabling the model signature or auditing the endpoint is equivalent to logging inference payloads, when only inference tables persist request and response data.
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
✓
Inference tables
Inference tables are the Databricks Model Serving feature that persists request and response payloads to a Delta table. Enabling them allows teams to join predictions with ground-truth labels over time, monitor drift, and debug production behavior. Signature enforcement, MLflow artifact logging, and audit logs serve different purposes and do not capture payloads.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Model signature enforcement
Why it's wrong here
The model signature validates input and output schemas at inference time but does not persist request or response payloads. It ensures the endpoint rejects malformed inputs, yet no historical records are stored for later analysis. Capturing payloads for monitoring requires a separate logging mechanism, so signature enforcement alone cannot satisfy the requirement to join predictions with labels.
- ✓
Inference tables
Why this is correct
Inference tables automatically log request and response payloads from a Model Serving endpoint into a Delta table in Unity Catalog. Each row records the timestamp, request, response, and metadata, enabling downstream joins with ground-truth labels and monitoring of data drift or model quality. Enabling inference tables is the supported way to capture payloads for later analysis.
- ✗
Endpoint access logs in the workspace audit log
Why it's wrong here
Audit logs record control-plane actions like endpoint creation, updates, and permission changes, not the actual request and response payloads. They show who changed what and when, but not the feature values or predictions. For payload capture, a data-plane logging feature is required, which audit logging does not provide.
- ✗
MLflow model logging to the run's artifact store
Why it's wrong here
MLflow model logging records artifacts such as the serialized model, conda environment, and signature at training time. It does not capture per-request inference payloads at serving time. While useful for reproducibility, this mechanism is unrelated to runtime traffic capture. The team needs a serving-time logging feature, not training-time artifact storage.
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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.