Databricks-ML-Pro ML Ops Practice Question
You are designing a strategy for monitoring model performance after deployment. Which of the following is the most important indicator that a model requires retraining?
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
✓
A significant drop in model prediction accuracy on production data.
Performance degradation, often caused by model drift or data drift, is the primary driver for retraining. While monitoring system metrics like latency is important, the predictive quality of the model is the ultimate metric. Detecting a significant drop in accuracy or precision indicates that the model is no longer meeting business requirements, triggering the need for a new training cycle in an automated MLOps workflow.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
An increase in the number of concurrent inference requests.
Why it's wrong here
Increased traffic is a scaling concern, not a model performance concern. It indicates that the model is becoming more popular, not that it is becoming less accurate. Scaling infrastructure to handle load is a separate operational task from monitoring the predictive capability of the model itself.
- ✓
A significant drop in model prediction accuracy on production data.
Why this is correct
Model accuracy is the bottom-line metric for performance. A drop in accuracy indicates that the relationship between inputs and outputs has changed, implying that the model is no longer reflecting the current reality of the data. This is the clearest, most urgent signal that the model requires retraining.
- ✗
The expiry of the model's 'Production' stage status.
Why it's wrong here
MLflow does not have an 'expiry' date for model stages. Models remain in their current stage until manually transitioned. Waiting for an arbitrary time-based trigger is an ineffective strategy, as it ignores the actual performance of the model, which could either be performing perfectly or failing catastrophically.
- ✗
A minor change in the underlying Databricks runtime version.
Why it's wrong here
While runtime changes can impact performance, they are not a direct signal that the model itself requires retraining. Retraining should be driven by data and performance metrics, not by infrastructure updates, unless specific compatibility issues are identified that render the model incapable of running on the new runtime.
About these practice questions
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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.