Databricks-ML-Pro ML Ops Practice Question
Your organization requires that all models deployed to production undergo a drift detection check. Which approach is most effective for monitoring model performance in Databricks?
⚠ Common exam trap
Candidates often suggest building custom monitoring dashboards using SQL or Python code, ignoring that Databricks Lakehouse Monitoring is the native, automated tool specifically built for drift detection.
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 Databricks Lakehouse Monitoring on the inference Delta tables to track drift automatically.
Monitoring drift requires comparing inference data distributions against the training baseline. Databricks Lakehouse Monitoring provides a managed service that automatically detects feature and prediction drift. By leveraging Delta tables as the source of truth, the monitoring service can compute statistics periodically and trigger alerts, ensuring that any degradation in model performance is identified quickly, allowing for proactive retraining or model rollback strategies.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Write a custom Spark job to compare the training table with inference logs every hour.
Why it's wrong here
Custom Spark jobs are difficult to maintain and integrate with alerting systems. They often lack the sophisticated drift detection algorithms provided by native tools like Lakehouse Monitoring. Building this from scratch introduces significant technical debt and management overhead that is unnecessary given available built-in platform capabilities.
- ✓
Enable Databricks Lakehouse Monitoring on the inference Delta tables to track drift automatically.
Why this is correct
Lakehouse Monitoring offers native, integrated drift detection that works directly with Unity Catalog and Delta tables. It provides out-of-the-box dashboards and automated alerts, reducing the need for custom code and ensuring that drift detection is consistently applied across all production models in the environment.
- ✗
Configure the Model Registry to automatically retrain the model whenever performance dips below a threshold.
Why it's wrong here
Model Registry is for versioning and lifecycle management, not for automated training triggering. Automatically retraining on a performance dip without human oversight is dangerous, as it may lead to 'model collapse' or the propagation of bad data, potentially causing further degradation in the production environment.
- ✗
Rely on end-user feedback to manually flag when the model performance seems degraded.
Why it's wrong here
User feedback is subjective, delayed, and unreliable. Relying on it for monitoring is a reactive approach that will result in significant downtime or bad user experiences. Robust MLOps requires automated, quantitative monitoring that triggers alerts before users even notice a problem with the model.
About these practice questions
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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.