Databricks-ML-Pro Model Deployment Practice Question
An ML engineer has registered a scikit-learn model in the Unity Catalog Model Registry and wants to serve it as a real-time endpoint using Databricks Model Serving. The model's MLflow signature expects a JSON payload with an array of records. The engineer creates a serving endpoint with a workload size of Medium and configures the served entity to use the latest model version. Which additional configuration is required to enable automatic payload logging to a Delta table for monitoring?
⚠ Common exam trap
Many exam-takers confuse delivery logs with inference tables; delivery logs track endpoint build events, not request/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 by specifying a Unity Catalog table location.
Automatic payload logging in Databricks Model Serving is achieved through inference tables, which require specifying a Unity Catalog table location when creating or updating the endpoint. This captures request and response data for monitoring and debugging. Other logging mechanisms like delivery logs or MLflow environment variables do not record inference 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.
- ✓
Enable inference tables on the endpoint by specifying a Unity Catalog table location.
Why this is correct
Inference tables capture request and response payloads for served models. To enable them, you must configure the endpoint with a Unity Catalog table location where logs are written. This allows monitoring and debugging without altering the model artifact. The other options do not provide payload logging.
- ✗
Set the environment variable MLFLOW_ENABLE_SYSTEM_METRICS_LOGGING to true in the model's conda environment.
Why it's wrong here
MLFLOW_ENABLE_SYSTEM_METRICS_LOGGING controls MLflow system metrics logging during training or batch inference, not automatic payload logging for a serving endpoint. It does not create Delta tables or capture request/response data for real-time inference. Thus it fails to meet the requirement.
- ✗
Attach a delivery log to the endpoint and specify a Delta table path.
Why it's wrong here
Delivery logs capture events about endpoint build and update status, not the actual inference request and response payloads. While useful for troubleshooting deployment issues, they do not log the data sent to and from the model. Therefore they do not satisfy the need for payload logging.
- ✗
Configure the model signature to include a 'log_payloads' parameter set to true.
Why it's wrong here
The model signature defines input and output schema; it does not include a parameter to enable payload logging. Signature is used for validation and schema enforcement, not for monitoring. Adding such a parameter would not trigger any logging behavior in Databricks Model Serving.
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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.