Databricks-DE-Assoc Working with Lakeflow Jobs Practice Question
Exhibit
{
"tasks": [
{
"task_key": "task1",
"notebook_task": { "notebook_path": "/nb1" },
"timeout_seconds": 3600
},
{
"task_key": "task2",
"depends_on": [{ "task_key": "task1" }],
"notebook_task": { "notebook_path": "/nb2" }
}
]
}Refer to the exhibit. If 'task1' fails due to a timeout, what happens to 'task2'?
⚠ Common exam trap
Candidates often assume that 'task2' will still run or that the job will retry automatically, ignoring the fact that downstream tasks in a DAG are strictly dependent on the success of upstream tasks.
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 will be skipped by the job scheduler.
In Databricks Jobs, dependencies are strictly evaluated based on the success of preceding tasks. If a task fails—regardless of whether it was a timeout or a code error—the dependent tasks in the directed acyclic graph (DAG) will not be triggered. This behavior ensures that downstream data quality is protected from incomplete or stale upstream data processing, which is a fundamental requirement for maintaining reliable data pipelines.
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 will run anyway because it is independent.
Why it's wrong here
The depends_on field explicitly creates a dependency relationship. Task2 is strictly bound to the successful completion of Task1. If Task1 encounters an error or reaches a timeout, the orchestrator will stop execution, preventing Task2 from starting, which prevents potential data corruption downstream in the pipeline.
- ✓
It will be skipped by the job scheduler.
Why this is correct
The scheduler evaluates the DAG status before executing each task. When a prerequisite task fails, the scheduler marks the dependent task as skipped. This prevents the execution of logic that relies on uncomputed results, maintaining the integrity of the data lineage and ensuring consistent output for downstream users.
- ✗
It will pause until task1 is restarted.
Why it's wrong here
Jobs do not automatically pause and wait for manual intervention on upstream failures. The scheduler treats the failure as a terminal state for the current run of that specific branch of the DAG, requiring an explicit user action to restart the failed job or the specific task.
- ✗
It will run with a 'warning' status.
Why it's wrong here
Databricks Jobs do not have a 'warning' status that allows dependent tasks to proceed after an upstream failure. The execution model is strictly binary: tasks either complete successfully or fail. Any failure terminates the current execution path for that branch, ensuring that subsequent tasks are not executed.
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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-DE-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-DE-Assoc exam.