Databricks-ML-Pro ML Ops Practice Question
You are responsible for monitoring a critical model deployed to Databricks Model Serving. You need to detect data drift and model performance degradation. Which TWO of the following actions should you take? (Choose two.)
⚠ Common exam trap
The trap here is focusing on infrastructure changes or manual comparisons instead of leveraging automated data capture and analysis tools designed for monitoring.
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
✓
Set up a Databricks SQL dashboard that queries the inference table and compares feature distributions to a baseline.
Inference tables capture the necessary request and response data for monitoring, while Databricks SQL dashboards enable analysis and alerting on that data. Together, they provide a robust solution for detecting data drift and model performance degradation. Other actions, such as changing instance types or isolating workspaces, do not address the monitoring requirements.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Deploy the model to a different workspace to isolate production traffic.
Why it's wrong here
Deploying to a different workspace does not help monitor drift or performance; it simply changes the environment. It could complicate monitoring by splitting data across workspaces. The goal is to centralize monitoring data and analysis, not isolate it. This action does not contribute to detecting data drift or model degradation.
- ✓
Set up a Databricks SQL dashboard that queries the inference table and compares feature distributions to a baseline.
Why this is correct
Databricks SQL dashboards can query inference tables to compute statistics and compare them against baseline distributions. This allows you to visualize drift and set up alerts. By leveraging SQL, you can create scheduled queries that detect significant deviations and trigger notifications, providing an effective monitoring solution without additional infrastructure.
- ✓
Enable inference tables on the model endpoint to log request and response data.
Why this is correct
Inference tables automatically capture the input features and output predictions for each request to a Model Serving endpoint. This data is essential for monitoring data drift and model performance, as it provides the raw material for comparison against baseline distributions and ground truth. Without inference tables, you would lack the necessary data to detect drift or degradation in real time.
- ✗
Use MLflow to log the model's training metrics and compare them to production metrics manually.
Why it's wrong here
MLflow tracks training metrics, but it does not automatically capture production metrics. Manually comparing training metrics to production is not a scalable or real-time monitoring solution. While MLflow can be part of a monitoring strategy, it lacks the automatic data capture needed for drift detection. Inference tables and dashboards provide a more direct and automated approach.
- ✗
Configure the endpoint to use a smaller instance type to reduce cost.
Why it's wrong here
Using a smaller instance type may reduce cost but does not aid in detecting data drift or model performance degradation. In fact, it could negatively impact latency and throughput. Monitoring drift and performance requires data collection and analysis, not changes to compute resources. This action is unrelated to the monitoring goals.
About these practice questions
One of 300 original Databricks-ML-Pro practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. 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.