Databricks-ML-Pro ML Ops Practice Question
A data scientist wants to automate the retraining of a model whenever new data arrives in a Delta table. They need to orchestrate a multi-step workflow that includes data validation, feature engineering, model training, and deployment. Which Databricks feature should they use?
⚠ Common exam trap
Watch out — candidates often confuse code packaging or data pipeline tools with orchestration; only Databricks Jobs provides the scheduling and dependency management needed for end-to-end ML workflows.
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
✓
Databricks Jobs with a multi-task workflow
Databricks Jobs with multi-task workflows is the built-in orchestration service that allows you to define dependencies, schedule triggers, and monitor execution of complex ML pipelines. It can be triggered by file arrival events, making it ideal for retraining when new data lands. Other options like MLflow Projects, Delta Live Tables, and Repos serve different purposes and lack native orchestration capabilities.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Databricks Jobs with a multi-task workflow
Why this is correct
Databricks Jobs support multi-task workflows, allowing you to define a directed acyclic graph of tasks with dependencies. You can schedule the job to trigger on a file arrival event or a schedule, and each task can run a notebook or Python script. This is the native orchestration tool in Databricks for building and automating ML pipelines, including retraining and deployment steps.
- ✗
Databricks Repos
Why it's wrong here
Databricks Repos enables version control and collaboration on code, but it does not provide orchestration or scheduling. It is a tool for managing source code, not for automating workflows. You could use Repos to store the code that a Job runs, but Repos itself does not trigger or coordinate tasks. Therefore, it is not the correct choice for orchestration.
- ✗
MLflow Projects
Why it's wrong here
MLflow Projects provide a format for packaging data science code, but they do not provide scheduling or orchestration. They are meant to be run manually or triggered by an external orchestrator. While you can use MLflow Projects within a Databricks Job, they alone cannot automate a multi-step workflow with dependencies and triggers. Thus, they are not the primary feature for orchestration.
- ✗
Delta Live Tables
Why it's wrong here
Delta Live Tables is designed for building reliable data pipelines with declarative transformations, but it is focused on data engineering, not ML model training and deployment. While you can use it for feature engineering, it does not natively orchestrate model training or deployment steps. For ML workflows, Databricks Jobs is more appropriate.
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.