Databricks-ML-Assoc ML Workflows Practice Question
An ML engineer is configuring a Databricks Job to retrain a model daily. The job must first run a notebook that creates a feature table, then run a notebook that trains and registers the model. The engineer wants to ensure the training notebook only runs if the feature table creation succeeds. Which Databricks Workflows feature should they use to define this dependency?
⚠ Common exam trap
The trap here is thinking that run_if conditions alone can establish dependencies without explicitly listing upstream tasks in depends_on.
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
✓
Set the training task's depends_on field to the feature table task.
In Databricks Workflows, task dependencies are defined using the depends_on field, which lists upstream tasks that must complete successfully before the task runs. This creates a DAG and ensures the training task executes only after the feature table task succeeds. Other options either do not establish the dependency or rely on additional configuration that still requires depends_on.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Set the training task's depends_on field to the feature table task.
Why this is correct
In Databricks Workflows, each task can specify a list of upstream tasks in its depends_on field. Setting the training task's depends_on to the feature table task ensures the training task runs only after the feature table task completes successfully. This creates a directed acyclic graph (DAG) that enforces the required order and success condition.
- ✗
Set the training task's run_if condition to ALL_SUCCESS, which automatically includes the feature table task.
Why it's wrong here
The ALL_SUCCESS run_if condition means the task runs only if all upstream tasks succeed, but it still requires the upstream tasks to be defined in depends_on. ALL_SUCCESS does not automatically include other tasks; it is a condition applied to the tasks listed in depends_on. Without depends_on, the training task has no upstream tasks and would run independently.
- ✗
Use a conditional task with a run_if condition based on the feature table task's output.
Why it's wrong here
Conditional tasks with run_if are used to skip or run tasks based on the status of upstream tasks, but they are not the primary mechanism for defining simple success dependencies. While run_if can be used to run a task only if an upstream task succeeded, the standard way to enforce a dependency is through depends_on. The question asks for the feature to define the dependency, which is depends_on.
- ✗
Configure the feature table task to call the training notebook at the end of its run.
Why it's wrong here
Calling the training notebook from within the feature table task would couple the two notebooks and bypass the Workflows orchestration. This approach does not leverage task dependencies or provide separate task status, retries, or monitoring. It also makes it harder to manage the workflow as a DAG. The correct approach is to define separate tasks with a 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.