Databricks-DE-Assoc Working with Lakeflow Jobs Practice Question
A data engineer needs to configure a Databricks Job containing multiple tasks where downstream tasks should only execute if all upstream parent tasks complete successfully. Which task dependency setting should be configured?
⚠ Common exam trap
Candidates sometimes try to manage task sequencing using external orchestration tools instead of native Databricks dependency graphs within the job configuration.
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
✓
Add the upstream tasks as parents of the downstream task directly within the job configuration graph.
To ensure tasks execute conditionally based on the success of parent tasks, you configure task dependencies within the Databricks Jobs UI or JSON definition. By default, adding a parent task creates a strict dependency where downstream tasks trigger only upon successful completion. This mechanism orchestrates complex directed acyclic graphs for robust, 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.
- ✗
Configure a retry policy with exponential backoff on the parent task to guarantee eventual success.
Why it's wrong here
Retry policies handle transient failures by repeating failed tasks rather than defining logical execution paths or conditional dependencies for downstream workloads. They do not control whether subsequent tasks in the DAG are allowed to start executing.
- ✗
Set up a global workspace webhook to trigger downstream tasks manually after the parent completes.
Why it's wrong here
A webhook fires external HTTP endpoints; it cannot express task-level dependency semantics, so downstream tasks would not wait on parent success. Webhooks suit alerting or invoking external systems on job events, whereas the required behaviour is configured through each task's depends_on relationships within the job definition.
- ✓
Add the upstream tasks as parents of the downstream task directly within the job configuration graph.
Why this is correct
Declaring upstream tasks as parents of the downstream task in the job graph creates explicit dependencies, so Databricks triggers the downstream task only after every parent completes successfully. This directly enforces the all-upstream-success condition the scenario requires.
- ✗
Enable concurrent run suppression on the job level to prevent overlapping workflow executions.
Why it's wrong here
Concurrent run suppression manages how the overall job behaves when a new trigger arrives while a previous run is still active. It affects global job concurrency rather than controlling individual task execution sequences within a run.
About these practice questions
This Databricks-DE-Assoc question is part of Courseiva's 276-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. 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-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.