Databricks-DE-Assoc Working with Lakeflow Jobs Practice Question
What is the primary function of the 'Retries' setting in a Databricks Job task?
⚠ Common exam trap
Candidates often confuse the retries setting with data quality error handling or schema auto-repair, missing that it specifically addresses transient infrastructure failures.
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
✓
To automatically recover from transient failures.
The 'Retries' setting is a robust mechanism for handling transient failures, such as network timeouts or temporary cloud provider issues, without manual intervention. By configuring retries, engineers increase the resilience of their pipelines. This is a critical best practice in production environments where external dependencies may be unstable, as it minimizes the need for on-call support and ensures that jobs eventually succeed despite minor, non-permanent infrastructure hiccups.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
To increase the task execution speed.
Why it's wrong here
Retries do not improve the underlying performance or speed of a task. They simply provide a mechanism to attempt the task again if it encounters an initial failure, ensuring that intermittent errors do not permanently stop the workflow execution, which is unrelated to the actual computational efficiency.
- ✓
To automatically recover from transient failures.
Why this is correct
Retries are explicitly designed to recover from transient, non-deterministic errors. By automatically re-running a task after a failure, the job platform increases the overall reliability of the pipeline, ensuring that temporary external outages do not cause a complete workflow failure that would otherwise require manual intervention.
- ✗
To allow tasks to run in parallel.
Why it's wrong here
Parallelism is controlled by the DAG structure and cluster capacity, not by retry settings. Retries relate to sequential attempts of the same task instance in the event of an error and have no influence on the scheduling or concurrent execution of different tasks within a job.
- ✗
To change the cluster node type.
Why it's wrong here
Retry settings are purely focused on the lifecycle management of a single task instance. They do not have the capability to alter the underlying infrastructure or compute resources of the cluster. Changing cluster configurations requires distinct operations within the Job or Cluster settings menus.
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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.