Databricks-ML-Pro ML Ops Practice Question
You are monitoring a production model deployed on Databricks Model Serving. You notice that the model's predictions have gradually become less accurate over time, likely due to data drift. You need to implement a solution that automatically detects drift and triggers retraining. Which Databricks feature should you use to monitor the model's input data and performance?
⚠ Common exam trap
Candidates often confuse data lineage or manual tracking with automated drift detection, but only Lakehouse Monitoring provides out-of-the-box statistical monitoring for production models.
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
✓
Databricks Lakehouse Monitoring for model quality and data drift, configured on the inference table that logs requests and responses.
Databricks Lakehouse Monitoring is the appropriate feature to monitor model input data and performance for drift. It can be configured on inference tables to automatically compute drift metrics and alert when thresholds are exceeded, enabling timely retraining. Other options lack the automated drift detection and integration with production workflows required for this scenario.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Databricks SQL dashboards that query the inference table and display accuracy metrics.
Why it's wrong here
SQL dashboards can visualize metrics but do not automatically detect drift or trigger actions. They require manual interpretation and do not provide statistical drift detection. While useful for reporting, they are not a substitute for automated monitoring features like Lakehouse Monitoring, which includes built-in drift metrics and alerts.
- ✗
MLflow Tracking to log prediction results and manually compare distributions over time.
Why it's wrong here
MLflow Tracking is designed for experiment tracking during development, not for production monitoring. While you can log predictions, it lacks automated drift detection and alerting. Manually comparing distributions is not scalable or timely for production. A dedicated monitoring solution like Lakehouse Monitoring is required for automated drift detection.
- ✓
Databricks Lakehouse Monitoring for model quality and data drift, configured on the inference table that logs requests and responses.
Why this is correct
Databricks Lakehouse Monitoring can be configured on inference tables to track data drift and model performance metrics over time. It provides automated alerts and can trigger retraining workflows. This is the native Databricks solution for monitoring model quality in production, integrating with the lakehouse for scalable analysis.
- ✗
Unity Catalog lineage to track data transformations and identify changes in input data sources.
Why it's wrong here
Unity Catalog lineage helps understand data provenance but does not monitor model input distributions or performance over time. It cannot detect drift or trigger retraining. Lineage is about data flow, not statistical monitoring. For drift detection, you need a monitoring solution that analyzes the actual data distributions and model outputs.
About these practice questions
This Databricks-ML-Pro question is part of Courseiva's 300-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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.