Databricks-DE-Assoc Working with Lakeflow Jobs Practice Question
A data engineer has configured a Databricks Job with multiple dependent tasks forming a linear pipeline. Task A extracts data, Task B transforms it, and Task C loads it into a gold table. The pipeline runs daily. The team notices that if Task B fails due to an intermittent schema validation issue, the entire job run fails, but they want Task C to execute conditionally only if Task B succeeds, while alerting the on-call engineer immediately upon any failure. How should the task dependencies and conditional execution be configured?
⚠ Common exam trap
Candidates incorrectly assume that failing upstream tasks automatically trigger downstream clean-up tasks, failing to explicitly configure success-only dependencies.
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
✓
Ensure Task C has a dependency pointing exclusively to Task B, and configure a job-level email notification for failures.
Configuring explicit task dependencies via the UI or API ensures that downstream tasks only execute upon the successful completion of their parents. By linking Task C strictly to Task B's success, you prevent corrupting downstream layers with partial or failed upstream transformations. This workflow pattern is essential for maintaining reliable data engineering pipelines in production Lakeflow environments.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Set Task C to run on failure of Task B to capture the exception state before alerting.
Why it's wrong here
Configuring a task to run only on failure is typically used for corrective or logging workflows, not for executing the primary downstream data loading step which requires valid transformed data to maintain lakehouse integrity.
- ✗
Convert Task B and Task C into a single notebook task and handle exceptions internally with try-except blocks.
Why it's wrong here
Bundling distinct transformation and loading steps into a monolith obfuscates operational monitoring in the Databricks Jobs UI, making it difficult to isolate failures at the individual task level and manage retries granularly.
- ✓
Ensure Task C has a dependency pointing exclusively to Task B, and configure a job-level email notification for failures.
Why this is correct
Establishing a direct parent-child dependency between Task B and Task C guarantees that the load step never runs prematurely. Adding job-level failure alerts ensures immediate notification to the engineering team without compromising pipeline DAG semantics.
- ✗
Disable the task dependency between Task B and Task C and run them in parallel with different cluster configurations.
Why it's wrong here
Running transformation and loading tasks in parallel violates the logical sequence of data pipelines, causing the load task to execute against stale or missing data generated by the transformation task.
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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.