Databricks-ML-Pro ML Ops Practice Question
A data scientist has registered a model in Unity Catalog under the name `prod.ml.forecast_model`. They now need to define a service-level objective (SLO) that automatically monitors the model's prediction quality in production, not just endpoint uptime. Which Databricks feature should they configure to detect model performance degradation?
⚠ Common exam trap
The trap here is assuming that serving endpoint health checks or autoscaling provide model quality monitoring, when they only cover infrastructure availability and capacity.
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
✓
Lakehouse Monitoring on the inference table
Lakehouse Monitoring on the inference table is the only option that provides statistical monitoring of model inputs and outputs. It computes drift and profile metrics and, when ground truth labels are available, model quality metrics such as accuracy or RMSE. This makes it the correct mechanism for an SLO tied to prediction performance rather than simple endpoint availability.
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 webhooks
Why it's wrong here
Registry webhooks trigger on model version lifecycle events such as transition requests or stage changes. They are event notifications, not continuous statistical monitors. They cannot compute accuracy, drift, or any prediction-quality metric over time, so they are unsuitable for an SLO on model performance.
- ✗
Cluster autoscaling policies on the serving cluster
Why it's wrong here
Autoscaling adjusts compute capacity based on load. It has no visibility into prediction correctness or data distribution. Configuring autoscaling might improve latency under traffic spikes, but it does nothing to detect or alert on model quality degradation, which is the core requirement here.
- ✗
Model Serving endpoint health checks
Why it's wrong here
Endpoint health checks verify that the serving container is responsive and can return HTTP 200. They do not evaluate prediction accuracy or drift against ground truth. While important for availability, they cannot detect silent performance degradation, so they fail to meet the stated SLO requirement.
- ✓
Lakehouse Monitoring on the inference table
Why this is correct
Lakehouse Monitoring is Databricks' native solution for tracking data and model quality drift. By creating a monitor on the Delta inference table that stores model inputs and predictions, you get automatic profile and drift metrics, including model quality metrics when ground truth is joined. This directly satisfies the requirement to monitor prediction quality, not just infrastructure health.
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.