Databricks-ML-Pro ML Ops Practice Question
Which THREE of the following are key responsibilities of an MLOps engineer when managing a model lifecycle on Databricks?
⚠ Common exam trap
Candidates frequently select ad-hoc data science tasks like hyperparameter tuning or feature engineering as primary MLOps responsibilities, ignoring governance, CI/CD promotion automation, and production 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
✓
Automating the model registration and promotion process in CI/CD.
MLOps engineers act as the bridge between data science and production operations. Their core duties involve automating the lifecycle, ensuring that data quality is monitored, and maintaining the infrastructure security and governance required to serve models reliably. These activities ensure that machine learning remains a stable and repeatable business function rather than an ad-hoc, error-prone research endeavor.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Automating the model registration and promotion process in CI/CD.
Why this is correct
Automation is at the heart of MLOps. By scripting the model registry interactions within CI/CD pipelines, engineers ensure that models move through stages consistently, preventing manual errors and ensuring that the promotion process is documented and repeatable across the organization's different deployment environments.
- ✗
Writing the core machine learning algorithms for every project.
Why it's wrong here
The MLOps engineer supports the data scientists who write the algorithms. Their job is to scale the process, not to do the research or algorithm development. If they are writing all the algorithms, they are failing to provide the infrastructure that allows data scientists to work independently.
- ✓
Monitoring the production health and data drift of deployed models.
Why this is correct
Model health is not static. MLOps engineers configure monitoring tools to track performance metrics and data drift. This proactive approach allows teams to respond to issues quickly, ensuring that the model continues to perform as expected against live data after it has been deployed to production.
- ✓
Managing access control and governance of model artifacts.
Why this is correct
Governance is critical for enterprise security. MLOps engineers ensure that only authorized personnel can promote models and that audit logs are generated for all lifecycle actions. This protects the organization from unauthorized changes and ensures compliance with regulatory requirements regarding model development and deployment.
- ✗
Manually testing every model in the production environment.
Why it's wrong here
Manual testing in production is the antithesis of MLOps. Testing must be automated and performed in staging or pre-production environments using CI/CD pipelines. Manual intervention in production introduces risk and defeats the purpose of creating a scalable, automated pipeline that is both safe and efficient.
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.