Databricks-ML-Assoc ML Workflows Practice Question
An ML engineer is configuring a Databricks Job to retrain a model daily. The job must run a notebook that reads from a feature table, trains a model, and registers it to the Model Registry. The engineer wants to ensure that the job fails immediately if the model's accuracy drops below a threshold. Which approach should they use?
⚠ Common exam trap
Many candidates confuse monitoring or alerting with job failure; only a task that raises an exception will cause the job to fail.
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
✓
Add a task that runs a Python script to query the MLflow API and raise an exception if accuracy is below threshold.
To make the job fail when accuracy is below a threshold, the engineer should add a task that evaluates the metric and raises an exception. This task can run after training and before registration, ensuring the job stops and no subpar model is registered. This is a common pattern for implementing quality gates in ML pipelines.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Configure the job to send an email alert if accuracy is below threshold, but allow the job to succeed.
Why it's wrong here
Email alerts notify but do not fail the job. The requirement is for the job to fail immediately, so alerts alone are insufficient. The job would still complete successfully, potentially registering a subpar model. A failing task or condition is needed to halt the workflow.
- ✓
Add a task that runs a Python script to query the MLflow API and raise an exception if accuracy is below threshold.
Why this is correct
Adding a task that checks the model's accuracy via MLflow API and raises an exception will cause the job to fail if the threshold is not met. This is a straightforward way to enforce a quality gate within the job's task graph, ensuring that subsequent tasks or the job itself fail.
- ✗
Use Databricks Model Serving to monitor the model's accuracy and automatically roll back if it drops.
Why it's wrong here
Model Serving monitors endpoint performance and can trigger rollbacks, but it operates after deployment. The requirement is to prevent registration or fail the retraining job when accuracy is low during training. Model Serving does not control the training job's success or failure.
- ✗
Set a timeout on the training task so that it fails if accuracy is not reached within a time limit.
Why it's wrong here
A timeout fails the task based on duration, not on model accuracy. It cannot evaluate whether accuracy meets a threshold. The engineer needs a condition based on a metric, not on time. Timeouts are for long-running tasks, not quality gates.
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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.