Databricks-ML-Pro ML Ops Practice Question
A machine learning engineer needs to automate retraining of a model whenever new data lands in a Delta table. The retraining must run on a schedule, use a specific cluster configuration, and send an email alert on failure. Which Databricks feature should be used to orchestrate this workflow?
⚠ Common exam trap
The trap here is reaching for specialized ML features like MLflow Projects or Delta Live Tables when the requirements are basic scheduling, cluster control, and alerting that Databricks Jobs already provides.
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 notebook task, a schedule trigger, and an email notification on failure.
Databricks Jobs is the native orchestration service for scheduled notebooks and scripts. It supports specifying cluster configuration, scheduling, and failure notifications. The other options either lack scheduling, cannot run training code, or are designed for data pipelines rather than ML workflows, so they do not satisfy the combined requirements of schedule, cluster control, and alerting.
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 notebook task, a schedule trigger, and an email notification on failure.
Why this is correct
Databricks Jobs provide scheduling, cluster specification, and notification configuration in one place. A notebook task can read the Delta table, retrain, and register the model. Schedule triggers and failure notifications are built-in, directly meeting all stated requirements without external orchestration tools.
- ✗
An MLflow Project run triggered by a file arrival event in DBFS using a filesystem watcher.
Why it's wrong here
MLflow Projects package code and environments but do not include a scheduler or file-arrival trigger. DBFS does not provide a native filesystem watcher that launches MLflow runs. This option conflates packaging with orchestration and would require custom infrastructure to detect new data and start runs.
- ✗
A Delta Live Tables pipeline with a continuously running mode that trains the model in a streaming table.
Why it's wrong here
Delta Live Tables is designed for declarative data pipelines, not model training orchestration. While it can run Python in a pipeline, it lacks native MLflow integration, failure email alerts, and the flexible cluster configuration expected for training. Using it here would be an unnatural fit and would not cleanly satisfy the alerting requirement.
- ✗
A Databricks SQL dashboard with a scheduled refresh that calls a stored procedure to retrain the model.
Why it's wrong here
Databricks SQL dashboards visualize query results and can refresh on a schedule, but they are not designed to run Python training code or manage MLflow runs. Stored procedures in Databricks SQL cannot execute arbitrary scikit-learn training. This option misuses a BI feature for an ML orchestration task.
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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.