Databricks-ML-Assoc ML Workflows Practice Question
An ML engineer is using Databricks Jobs to orchestrate a pipeline that includes a notebook for feature engineering and a notebook for model training. The training notebook must run only after the feature engineering notebook completes successfully, and both must run on a schedule. Which configuration in Databricks Jobs achieves this dependency?
⚠ Common exam trap
The trap here is thinking that separate jobs or time-based scheduling can enforce a dependency, when only a multi-task job with depends_on guarantees sequential execution on success.
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
✓
Create a multi-task job where the training task has a depends_on condition referencing the feature engineering task.
Databricks Jobs multi-task workflows allow defining tasks with dependencies using depends_on. This ensures the training task starts only after the feature engineering task succeeds, and the entire job can be scheduled. Separate jobs with triggers, %run, or time-based scheduling do not provide the same reliable dependency semantics.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a single notebook that calls the feature engineering notebook and then the training notebook using %run.
Why it's wrong here
%run executes another notebook inline, but it does not provide task-level dependency management, retries, or separate cluster configuration. It also runs in the same cluster and execution context, which may not be desired. This approach lacks the robustness and scheduling capabilities of a multi-task job.
- ✗
Set the training notebook to run on a schedule that starts after the expected completion time of the feature engineering notebook.
Why it's wrong here
Time-based scheduling does not guarantee that the feature engineering notebook has completed successfully; it may fail or run long. This approach is brittle and can lead to training on stale or incomplete data. A dependency-based trigger is required for correctness.
- ✓
Create a multi-task job where the training task has a depends_on condition referencing the feature engineering task.
Why this is correct
Databricks Jobs supports multi-task workflows with dependencies. By setting depends_on for the training task to the feature engineering task, the training task runs only after the feature engineering task succeeds. This is the native, supported way to express task dependencies within a job, and the job can be scheduled.
- ✗
Create two separate jobs and configure the training job to be triggered by the completion of the feature engineering job using a job run trigger.
Why it's wrong here
While job run triggers can chain jobs, this approach is more complex and does not express the dependency within a single job. It also requires configuring a trigger on the training job that listens for the feature engineering job's success. The multi-task job with depends_on is simpler and directly models the dependency.
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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-Assoc 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-Assoc exam.