Databricks-ML-Pro ML Ops Practice Question
Exhibit
job_config.json:
{
"tasks": [
{
"task_key": "train_model",
"notebook_task": {
"notebook_path": "/training"
}
},
{
"task_key": "evaluate_model",
"depends_on": [{"task_key": "train_model"}],
"notebook_task": {
"notebook_path": "/evaluation"
}
}
]
}Refer to the exhibit. If the 'train_model' task fails, what happens to the 'evaluate_model' task in this Databricks Job?
⚠ Common exam trap
Candidates assume the job continues regardless of failure, not realizing that Databricks Jobs default to a strict dependency model where downstream tasks are aborted if the parent fails.
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
✓
It is skipped because the dependency is not met.
In Databricks Jobs, a task that depends on another will only execute if the parent task finishes successfully. If 'train_model' fails, the dependency condition for 'evaluate_model' is not met, causing the job to stop or skip the downstream task. This behavior is intentional to prevent evaluating a non-existent or corrupted model, ensuring pipeline integrity and avoiding wasted computational resources on dependent tasks.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
It executes regardless of the failure.
Why it's wrong here
The dependency configuration explicitly requires 'train_model' to complete successfully. The scheduler enforces this logic; it will not trigger the evaluation notebook if the upstream training notebook reports a non-zero exit code or fails to complete, as the downstream task depends on the successful output of the former.
- ✓
It is skipped because the dependency is not met.
Why this is correct
The 'depends_on' field creates a strict prerequisite. When the parent task fails, the DAG execution stops or skips the child task by design. This mechanism protects the pipeline from executing logic based on incomplete or invalid model artifacts, maintaining the reliability of the overall MLOps training workflow.
- ✗
It attempts to run using the last successful model artifact.
Why it's wrong here
Databricks Jobs do not automatically fallback to previous artifacts unless specifically configured with complex logic. By default, the task simply fails or skips. Assuming it would automatically pick up a previous version is dangerous as it would lead to evaluating stale models against current training data.
- ✗
It retries the 'train_model' task automatically until success.
Why it's wrong here
Retries must be explicitly defined in the task configuration. The provided JSON does not include a retry policy. Without a defined retry count, the system treats a single failure as a terminal state for the task, meaning the dependent task will remain blocked and the job will report failure.
About these practice questions
One of 300 original Databricks-ML-Pro practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
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.