Databricks-DE-Pro Debugging and Deploying Practice Question
A data engineer is troubleshooting a Databricks Workflow where a downstream task relies on an upstream task's output. Which TWO actions ensure the data dependency is correctly handled during a failure scenario?
⚠ Common exam trap
Candidates often confuse workflow task configuration attributes like 'depends_on' with general cluster settings, or forget that 'repair and rerun' requires specific task targeting instead of restarting the entire pipeline from scratch.
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
✓
Configure the downstream task to use the 'depends_on' attribute to reference the upstream task ID.
Proper dependency management ensures that downstream tasks do not attempt to process incomplete or missing data from upstream failures. By using task dependencies and repair-and-rerun capabilities, engineers can isolate failures and maintain data integrity. These features are critical in production pipelines to prevent downstream corruption and ensure that data lineage remains consistent throughout the workflow execution cycle regardless of individual component failures.
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 the downstream task to use the 'depends_on' attribute to reference the upstream task ID.
Why this is correct
The 'depends_on' attribute explicitly defines the task DAG structure within Databricks Workflows. By creating this dependency, the scheduler guarantees that the downstream task will only execute if the upstream task succeeds, preventing erroneous runs when input data is missing or corrupted due to preceding failures.
- ✗
Set the task timeout to zero to prevent the workflow from ever stopping during a failure.
Why it's wrong here
Setting a timeout to zero is not a valid configuration for ensuring data integrity during dependencies. It essentially disables the monitoring of task execution time, which can lead to hung jobs consuming resources indefinitely without triggering the necessary alerts or failure handling logic required for production stability.
- ✓
Enable the 'repair and rerun' feature to target only the failed tasks in the pipeline.
Why this is correct
The repair and rerun feature allows engineers to execute only the failed tasks and their downstream dependents. This preserves the existing successful outputs of the workflow and ensures that the system only attempts to process data that was previously blocked by the specific task failure event.
- ✗
Hardcode the file path of the upstream output into the downstream task configuration.
Why it's wrong here
Hardcoding paths is an anti-pattern that couples tasks tightly and creates maintenance challenges. It ignores the workflow dependency graph, making it impossible for the scheduler to manage retries or state correctly. If the upstream task fails, the downstream task might still try to read non-existent data.
- ✗
Disable all retries to ensure that the error log is captured immediately upon failure.
Why it's wrong here
Disabling retries does not assist in managing dependencies; it only guarantees the job stops at the first sign of trouble. While it might assist in capturing logs, it fails to utilize Databricks' built-in resilience features that handle transient errors automatically, thus reducing overall workflow reliability and efficiency.
About these practice questions
This Databricks-DE-Pro question is part of Courseiva's 267-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-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-DE-Pro exam.