Databricks-ML-Pro ML Ops Practice Question
You are implementing model monitoring to detect data drift. Which Databricks feature should you use to automatically track and alert on changes in the distribution of input data features over time?
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
Databricks Lakehouse Monitoring provides automated insights into data quality and drift. By tracking statistical properties of incoming data against a baseline, it generates alerts when significant shifts are detected. This is a crucial component of MLOps, as it proactively identifies when a model's performance might degrade due to environmental changes, allowing teams to retrain or adjust models before they negatively impact business outcomes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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MLflow Experiment Tracking
Why it's wrong here
MLflow is for recording training results, not for monitoring production data distribution. While it can store model performance, it does not have built-in features to calculate statistical drift metrics on production data or trigger alerts based on input distribution changes in a live system.
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Delta Live Tables Expectations
Why it's wrong here
Expectations in Delta Live Tables are used for data quality checks at the row level during ingestion. They are effective for catching bad data formats or null values, but they are not designed for calculating complex statistical drift metrics on features for ML models.
- ✓
Databricks Lakehouse Monitoring
Why this is correct
Lakehouse Monitoring is the purpose-built service for detecting drift in input features and model predictions. It automatically creates dashboards and alerts by comparing production data against training baselines, providing essential visibility into model health after deployment in a production environment.
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Databricks SQL Alerts
Why it's wrong here
SQL Alerts are generic triggers based on query outcomes. While you could technically write complex SQL to calculate drift metrics, it is not a specialized MLOps tool. It lacks the automated, integrated drift detection capabilities built into the dedicated Lakehouse Monitoring service.
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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.