Databricks-ML-Pro ML Ops Practice Question
A machine learning engineer is responsible for monitoring a production model deployed to Databricks Model Serving. The model predicts customer churn and is served via a REST endpoint. The engineer needs to detect data drift and model performance degradation over time. Which TWO actions should the engineer take to enable effective monitoring? (Choose two.)
⚠ Common exam trap
The trap here is thinking that training accuracy or automatic retraining can substitute for actual production monitoring, when in fact you must capture and analyze live inference data to detect drift and degradation.
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
✓
Enable inference logging on the model serving endpoint to capture request and response payloads.
Effective monitoring of a production model requires capturing inference data and analyzing it for drift and performance. Enabling inference logging provides the raw data, while scheduling a job to compute drift metrics against the training set enables detection of data drift. Together, these actions allow the engineer to identify when the model's input distribution diverges from training, which is a key signal of degradation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable automatic model retraining triggered by any change in the input data schema.
Why it's wrong here
Automatic retraining on schema changes alone is not a monitoring action and can be risky; schema changes may not indicate performance degradation. Monitoring should first detect drift and performance issues, and then retraining can be considered as a remediation step. This option does not address the need to detect drift and degradation, and could lead to unnecessary retraining without proper validation.
- ✓
Enable inference logging on the model serving endpoint to capture request and response payloads.
Why this is correct
Inference logging captures the input features and model predictions for each request, which is essential for computing drift metrics and evaluating performance over time. Databricks Model Serving allows you to enable inference logging to a Delta table, where the data can be analyzed using Databricks SQL or notebooks. Without this logging, there is no historical record of production data to compare against training data or to compute accuracy metrics, making drift detection impossible.
- ✗
Use the model's training accuracy as a proxy for production performance and set up alerts based on that metric.
Why it's wrong here
Training accuracy is not a reliable indicator of production performance because it does not account for data drift or changes in the underlying data distribution. Relying on it can mask degradation. Monitoring should focus on production data and, when labels are available, on live performance metrics. Using training accuracy as a proxy would fail to detect issues such as concept drift or feature drift that occur after deployment.
- ✗
Configure the endpoint to use a smaller instance type to reduce cost, as monitoring does not require additional resources.
Why it's wrong here
Reducing instance size does not aid monitoring and may actually hinder performance if the endpoint becomes overloaded. Monitoring requires capturing and storing inference data, which can be resource-intensive. The goal is to enable observability, not to cut costs at the expense of data collection. A smaller instance could lead to increased latency or failed requests, undermining the monitoring effort.
- ✓
Schedule a Databricks job to periodically compute drift metrics by comparing logged inference data with the training dataset.
Why this is correct
Computing drift metrics such as population stability index (PSI) or KL divergence requires comparing the distribution of production features with the training data. A scheduled job can query the inference logs and the training set, calculate drift, and trigger alerts if thresholds are exceeded. This proactive approach enables timely detection of data drift, which is a leading indicator of model degradation, and is a standard practice in MLOps on Databricks.
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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.