Databricks-ML-Pro ML Ops Practice Question
You are designing a model retraining strategy. What is the most reliable way to trigger a retraining job based on model performance degradation?
⚠ Common exam trap
Test-takers often assume models retrain automatically based solely on elapsed time, overlooking that event-driven workflows triggered by drift alerts are the best practice.
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
✓
Use Databricks Workflows to trigger retraining based on drift detection alerts.
Linking monitoring metrics to workflow orchestration is the standard MLOps pattern for automated retraining. By configuring a monitor that triggers a Databricks Job when performance drifts below a defined metric (e.g., accuracy), you create a closed-loop system. This ensures that models are updated only when necessary, minimizing cost while maintaining high model performance and reducing manual intervention in the model lifecycle.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Schedule the retraining job to run every Monday regardless of current model performance.
Why it's wrong here
Time-based retraining is inefficient. It may trigger unnecessary jobs when the model is performing well, wasting resources, or fail to trigger when performance degrades unexpectedly between scheduled runs. Retraining should be event-driven based on actual performance metrics to be truly efficient and effective.
- ✓
Use Databricks Workflows to trigger retraining based on drift detection alerts.
Why this is correct
This event-driven approach ensures that retraining only occurs when it is objectively needed. By triggering Workflows based on drift detection, you maintain the model's relevance to the current data distribution without wasting compute budget on redundant retraining jobs when the model is still performing adequately.
- ✗
Send an email alert to the lead data scientist to manually kick off the job.
Why it's wrong here
Manual intervention is slow, error-prone, and doesn't scale. It creates bottlenecks in the deployment pipeline. The goal of MLOps is to automate the feedback loop so that models can recover their performance automatically, reducing the management burden and ensuring faster responses to changing data patterns.
- ✗
Write a cron job in the notebook that calculates performance every minute.
Why it's wrong here
Running a cron job inside a notebook is inefficient and unreliable. It competes for resources and lacks proper error handling and logging. Dedicated platform features like Databricks Workflows and Lakehouse Monitoring are designed for this purpose, offering built-in reliability, logging, and integration with the wider platform.
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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.