Databricks-ML-Pro ML Ops Practice Question
Which THREE of the following are primary responsibilities of an MLOps engineer when maintaining production ML models in Databricks?
⚠ Common exam trap
Candidates focus exclusively on model training metrics, neglecting essential MLOps operational tasks like drift monitoring, CI/CD, and security controls.
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
✓
Monitoring model performance and detecting data drift.
An MLOps engineer's role is to ensure stability, performance, and compliance. This includes monitoring model performance to detect drift, managing the CI/CD pipeline for automated deployments, and ensuring security via robust access controls. By balancing these tasks, they ensure that the machine learning system remains reliable and valuable to the business over time, effectively bridging the gap between development and operations.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Monitoring model performance and detecting data drift.
Why this is correct
Drift detection is critical to maintaining model accuracy. As data distributions change, models can lose predictive power. MLOps engineers must implement monitoring solutions to identify these shifts early, allowing for timely retraining and redeployment, which protects the organization from deploying degraded models to production users.
- ✗
Writing the core machine learning research papers for the organization.
Why it's wrong here
Writing research papers is the domain of research scientists. An MLOps engineer focuses on the operationalization, scaling, and reliability of the models rather than the theoretical research. Mixing these roles would distract from the core engineering duties required to maintain production uptime and infrastructure health.
- ✓
Establishing CI/CD pipelines to automate testing and deployment.
Why this is correct
Automation is the heart of MLOps. CI/CD pipelines eliminate manual deployment errors, enforce quality gates, and ensure that only code and models that pass tests reach production. This practice significantly increases the velocity of the team while simultaneously improving the overall stability of the production environment.
- ✓
Configuring workspace security and access control for model artifacts.
Why this is correct
Security and governance are mandatory in enterprise environments. MLOps engineers ensure that only authorized personnel can promote models to production or access sensitive training data. This prevents unauthorized changes and protects intellectual property, complying with corporate security mandates and minimizing the risk of internal threats.
- ✗
Manually retraining every model daily regardless of performance metrics.
Why it's wrong here
Blindly retraining models is inefficient and costly. MLOps focuses on data-driven retraining triggers based on performance metrics or data drift. Manual, arbitrary retraining creates technical debt and unnecessary compute expenses, undermining the cost-efficiency goals of a well-architected MLOps strategy within the Databricks platform.
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.