Databricks-DE-Assoc Working with Lakeflow Jobs Practice Question
A data engineer wants to ensure that a Databricks Job task only runs if the preceding task completes successfully, but needs to add a specific timeout threshold for this individual task. Where should this configuration be applied?
⚠ Common exam trap
Candidates often look for cluster-level settings or global workspace configurations, incorrectly assuming that timeouts and retries are managed outside the specific task definition.
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
✓
Within the individual task definition.
In Databricks Jobs, task-level configurations such as timeouts, retries, and dependencies are defined within the task definition itself. By specifying a timeout in the task settings, the engineer prevents runaway processes from consuming cluster resources indefinitely. Understanding this granularity is vital for cost management and workflow reliability, as it allows engineers to set different SLAs for ingestion versus transformation tasks within a single orchestrated pipeline.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
At the job level settings.
Why it's wrong here
Job level settings apply to the entire workflow, including notifications and overall job timeouts. Applying a timeout here would affect all tasks equally, preventing the granular control needed for specific processing steps that might legitimately take longer than others during the execution of the pipeline.
- ✓
Within the individual task definition.
Why this is correct
Task definitions contain specific configurations like timeout_seconds, retries, and depends_on arrays. Setting the timeout here ensures that only this specific task is terminated if it exceeds the threshold, while allowing downstream tasks to potentially trigger or fail gracefully based on the defined job dependency graph.
- ✗
Inside the cluster configuration JSON.
Why it's wrong here
Cluster configurations manage the compute resources, such as node types, autoscaling, and spark configurations. While you can set environment variables or spark properties here, timeout logic for a job task is orchestrated by the Jobs service control plane, not the underlying compute cluster runtime itself.
- ✗
Using a global Workspace policy.
Why it's wrong here
Workspace policies enforce constraints on compute resources like instance types or pricing tiers to manage costs and compliance. They do not handle workflow orchestration logic or task-level execution parameters like timeouts, which are specific to the Jobs scheduler service and its defined task dependency trees.
About these practice questions
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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.