Databricks-ML-Assoc Model Deployment Practice Question
A data science team is deploying a model to a Databricks Model Serving endpoint. They want to enable inference logging to capture the input data and predictions for monitoring and debugging. Which TWO configurations are required to enable inference logging for a serving endpoint? (Choose two.)
⚠ Common exam trap
The trap here is assuming that model-level configurations like output format or signature are needed for inference logging, when actually it is a serving endpoint feature requiring explicit enablement and a storage table.
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
✓
Specify a Delta table to store the inference logs.
To enable inference logging for a Model Serving endpoint, you must explicitly enable it in the endpoint configuration and specify a Delta table to store the logs. The endpoint's identity needs write access to that table. Other factors like workload size or model signature do not affect logging. This allows capturing request and response data for monitoring and debugging.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Set the endpoint's workload size to at least 'Medium'.
Why it's wrong here
Workload size affects compute resources and performance, but it is not related to enabling inference logging. Logging can be enabled on any workload size, from Small to Large. Therefore, this is not a requirement for inference logging.
- ✗
Ensure the model signature includes a field for logging.
Why it's wrong here
The model signature defines input and output schemas, not logging capabilities. Inference logging is a serving feature that operates independently of the signature. Adding a field for logging in the signature would not enable logging and is not required.
- ✓
Specify a Delta table to store the inference logs.
Why this is correct
Inference logging requires a target Delta table where the logs will be written. The table must be specified in the endpoint configuration, and the endpoint's service principal must have write permissions to it. This table stores the request and response data for monitoring.
- ✓
Enable inference logging in the endpoint configuration.
Why this is correct
Inference logging must be explicitly enabled when creating or updating the serving endpoint. This is done by setting the 'inference_table_config' or similar setting in the endpoint configuration. Without enabling it, no logs are captured, regardless of other settings.
- ✗
Configure the model to output logs in JSON format.
Why it's wrong here
The model's output format does not determine whether inference logging is enabled. Logging is handled at the serving layer, which captures the raw input and output regardless of the model's internal logging. Thus, this is not a required configuration.
About these practice questions
Courseiva writes every Databricks-ML-Assoc question from scratch — 319 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
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.