Databricks Data Engineer Associate Question 423
Select 4You are designing a Databricks Job to process large amounts of data in a pipeline. The pipeline consists of three stages: data ingestion, transformation, and machine learning model training. Why might you use multiple tasks within a single Job to implement this pipeline?
- A
To allow each stage of the pipeline to run independently and in sequence, ensuring clear dependencies between tasks.
- B
To optimize resource allocation for each stage by configuring different cluster settings per task.
- C
To avoid duplicated effort when re-running specific stages of the pipeline, as you can restart only the failed task.
- D
To minimize the overall cost of the pipeline by reducing the number of tasks and simplifying the workflow.
- E
To enable better monitoring and troubleshooting by isolating logs and metrics for each task.
Show answer and explanation
Correct answers: A, B, C, E
Explanation
Using multiple tasks in Databricks Jobs provides significant benefits for complex pipelines. It ensures clear task dependencies, allows for resource optimization through individual task-level configurations, supports task-level retries for fault tolerance, and improves monitoring and troubleshooting through isolated logs and metrics. These advantages make workflows more efficient, reliable, and manageable.
- A. Correct.
Using multiple tasks allows you to define dependencies and ensure each stage runs in sequence, which is essential for structured pipelines.
- B. Correct.
Different tasks allow for customized cluster configurations, enabling resource optimization for each stage's specific requirements.
- C. Correct.
With multiple tasks, you can restart only the failed tasks without re-running the entire pipeline, saving time and effort.
- D. Incorrect.
Reducing the number of tasks does not minimize cost or simplify workflows in large, multi-stage pipelines. Multiple tasks allow for more control and optimization.
- E. Correct.
Isolating logs and metrics for each task makes it easier to monitor, debug, and identify issues in specific stages.