Databricks-ML-Assoc Model Deployment Practice Question
A team is preparing to deploy a registered MLflow model to a Databricks Model Serving endpoint. They want to capture every request and response for later monitoring and debugging, and they also want the endpoint to remain available during a rolling model version update. (Choose two.)
⚠ Common exam trap
The trap here is assuming that deleting and recreating the endpoint is required to change model versions, when Model Serving supports in-place version updates with rolling rollout.
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 serving endpoint.
Capturing every request and response is achieved by enabling inference tables, which log payloads to a Delta table. Keeping the endpoint available during a model version change is achieved by updating the served version in place, which triggers a rolling update rather than endpoint deletion. Together these satisfy both the observability and availability requirements without downtime or manual log plumbing.
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 verbose logging in the MLflow model's Python code and write logs to DBFS.
Why it's wrong here
Application-level logging inside the model writes only what the model code chooses to emit and does not systematically capture full request and response payloads. DBFS logs are also not the supported mechanism for endpoint request logging. This does not meet the requirement to capture every request and response in a governed, queryable location.
- ✓
Enable inference tables on the serving endpoint.
Why this is correct
Inference tables automatically log request payloads and response payloads from a Model Serving endpoint into a Delta table in Unity Catalog. Enabling them satisfies the requirement to capture every request and response for monitoring and debugging, and the logged data can be queried directly with SQL for drift analysis or troubleshooting.
- ✗
Delete the existing endpoint and create a new one pointing at the new model version.
Why it's wrong here
Deleting and recreating the endpoint causes downtime and loses endpoint-level configurations such as inference tables and permissions. It also resets any autoscaling warm replicas. This approach contradicts the requirement that the endpoint remain available during the model version update, so it is not appropriate for a rolling update.
- ✓
Configure the endpoint with zero-downtime updates by using a new model version in the same endpoint configuration.
Why this is correct
Model Serving endpoints support updating the served model version without tearing down the endpoint. When the configuration is updated to a new version, Databricks performs a controlled rollout so existing replicas continue serving traffic while new replicas initialize, keeping the endpoint available during the model version change.
- ✗
Set the endpoint's `min_instances` to 0 to reduce cost during the update.
Why it's wrong here
Setting `min_instances` to 0 allows the endpoint to scale down to zero replicas during idle periods, which introduces cold-start latency and can cause request failures during the update window. It does not provide zero-downtime behavior and is unrelated to capturing request and response payloads, so it fails both requirements in the scenario.
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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.