Databricks-ML-Pro ML Ops Practice Question
Your organization wants to monitor production models for drift. Which Databricks service should be used to detect changes in the input data distribution compared to the training data?
⚠ Common exam trap
Test-takers frequently look for manual logging solutions, missing the native Databricks Lakehouse Monitoring service designed explicitly for automated 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
✓
Databricks Lakehouse Monitoring
Databricks Lakehouse Monitoring is the native solution for tracking data quality and drift. By analyzing incoming data against a baseline established during training, it identifies statistical deviations. This is critical for MLOps because detecting drift early allows data scientists to trigger retraining or investigate data pipeline issues, ensuring model performance remains consistent over time despite changing real-world data patterns.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
MLflow Model Registry
Why it's wrong here
The Model Registry is for versioning and metadata management. It does not perform statistical monitoring or drift analysis on live inference data. While it can store metrics, it lacks the compute engine and analysis tools required to compare real-time data distributions against historic baselines.
- ✓
Databricks Lakehouse Monitoring
Why this is correct
Lakehouse Monitoring is specifically built to compute drift metrics by comparing production data with training baselines. It provides automated alerts and dashboards, enabling teams to proactively identify when input feature distributions shift, which is a primary cause of model performance degradation in production environments.
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Delta Live Tables (DLT)
Why it's wrong here
DLT is a framework for building data pipelines and ensuring data quality through expectations. While it can catch data quality issues like nulls or schema changes, it is not designed to perform statistical drift analysis between inference input and training distributions, which requires specialized ML monitoring capabilities.
- ✗
Databricks SQL Alerting
Why it's wrong here
Databricks SQL alerting is for tracking simple SQL-based conditions, such as 'row count > 1000'. It cannot perform complex statistical analysis required for drift detection, such as calculating Kullback-Leibler divergence or Kolmogorov-Smirnov tests. It is unsuitable for the mathematical rigor needed in machine learning monitoring.
About these practice questions
Courseiva writes every Databricks-ML-Pro question from scratch — 300 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or 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.