Databricks-ML-Pro ML Ops Practice Question
Which TWO of the following are primary benefits of using the MLflow Model Registry in Databricks?
⚠ Common exam trap
Candidates sometimes select 'model training' or 'artifact storage' as primary benefits, confusing general MLflow features with the specific governance and lifecycle management provided by the Model Registry.
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
✓
Lifecycle state tracking (e.g., Staging, Production).
The Model Registry centralizes model management, providing a single source of truth for model lifecycle stages and version history. This is vital for MLOps, as it ensures that teams can track which models are in production, who approved them, and how they perform over time, enabling consistent deployment workflows and improved auditability for regulatory compliance and internal governance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Automated training of models.
Why it's wrong here
The Model Registry is a storage and management system, not a training engine. Automated training is handled by Databricks Jobs and notebooks. While the registry stores the results of training, it does not have the capability to execute training code, manage resources, or handle compute for model training tasks.
- ✓
Lifecycle state tracking (e.g., Staging, Production).
Why this is correct
Tracking the state of a model version is a core feature of the registry. It allows teams to clearly define which models are ready for production, which are being tested, and which have been deprecated, ensuring that deployment pipelines only pull models from the appropriate, authorized lifecycle stages.
- ✓
Centralized versioning and lineage.
Why this is correct
Versioning ensures that every change to a model is recorded, allowing for easy rollbacks and historical comparisons. Lineage tracks the source notebook, data, and parameters for each version, providing a transparent and auditable record that is essential for debugging and ensuring the reproducibility of production-grade models.
- ✗
Real-time monitoring of inference latency.
Why it's wrong here
The Model Registry does not monitor inference latency. That is a task for the model serving endpoint metrics and monitoring dashboards. While the registry can store performance metrics, it does not provide the real-time observability required to identify production latency issues as they occur in live serving environments.
- ✗
Automatic data cleaning for training sets.
Why it's wrong here
The registry is for model objects, not data cleaning. Data cleaning is performed in the feature engineering and preprocessing stages of the ML pipeline, typically using Spark or DLT. The registry has no insight into the raw data or the logic used to clean it, making it unsuitable for this task.
About these practice questions
This Databricks-ML-Pro question is part of Courseiva's 300-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam 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.