Databricks-ML-Assoc Model Deployment Practice Question
A data scientist has registered a scikit-learn model in the Databricks Model Registry as `prod.churn_model`. The production endpoint serving this model must automatically roll back to the previously served version if the newly deployed version's error rate exceeds a threshold within one hour of deployment. Which Databricks feature should the data scientist configure to meet this requirement?
⚠ Common exam trap
The trap here is assuming Model Serving includes built-in automatic rollback policies based on error-rate thresholds, when in fact rollback must be orchestrated through monitoring and jobs.
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
✓
Set up a Databricks Lakehouse Monitoring quality monitor on the endpoint's inference table with a custom metric and an alert that triggers a rollback via a job.
Automatic rollback based on runtime error rate is not a built-in Model Serving feature. The viable path is to monitor the endpoint's inference table with Lakehouse Monitoring, define a custom metric for error rate, and use an alert to trigger a job that reconfigures the endpoint to the previous version. This leverages Databricks-native monitoring and orchestration to meet the requirement.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use MLflow Model Registry webhooks to trigger a rollback when the endpoint's error rate exceeds the threshold.
Why it's wrong here
MLflow Model Registry webhooks fire on registry events such as model version creation or stage transitions, not on runtime endpoint metrics like error rate. They cannot observe serving traffic or performance, so they cannot detect the threshold breach. A webhook could be part of a custom solution, but alone it does not provide automatic rollback based on error rate.
- ✓
Set up a Databricks Lakehouse Monitoring quality monitor on the endpoint's inference table with a custom metric and an alert that triggers a rollback via a job.
Why this is correct
This is the correct approach because Databricks Lakehouse Monitoring can compute custom metrics from the endpoint's inference table, and alerts can trigger a Databricks job. That job can then update the endpoint configuration to revert to the previous model version. This provides the required automatic, threshold-based rollback within the specified time frame using native Databricks capabilities.
- ✗
Configure the endpoint to serve the new version with a small percentage of traffic and manually promote it after validating the error rate.
Why it's wrong here
This approach is manual and does not automatically roll back. While traffic splitting can limit blast radius, it still requires a human to monitor and revert traffic. The requirement explicitly asks for automatic rollback within one hour, which this manual process does not provide. It also does not define an error-rate threshold or trigger.
- ✗
Enable automatic model version rollback on the serving endpoint by defining a rollback policy with the error-rate threshold.
Why it's wrong here
Databricks Model Serving does not provide an automatic rollback policy based on error-rate thresholds. While the endpoint can serve multiple versions and traffic can be split, the platform does not monitor error rates and revert traffic automatically. Implementing this behavior requires custom monitoring and orchestration, so this feature does not exist as described and cannot satisfy the requirement.
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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
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