Databricks Data Engineer Associate Question 425
Select 3A data engineering team is designing a Databricks Job to process customer data for a machine learning workflow. They decide to use multiple tasks within the Job instead of a single monolithic task. What are the benefits of using multiple tasks in this scenario?
- A
Improves the modularity of the workflow, making it easier to debug and manage.
- B
Allows tasks to run in parallel where possible, reducing overall execution time.
- C
Increases the likelihood of encountering cluster resource constraints due to multiple concurrent tasks.
- D
Enables the reuse of intermediate data outputs between tasks without recomputation.
- E
Eliminates the need for defining dependencies between different steps in the workflow.
Show answer and explanation
Correct answers: A, B, D
Explanation
Using multiple tasks in a Databricks Job provides several advantages, such as modularity, parallel execution, and the ability to reuse intermediate outputs. These benefits enhance the manageability, performance, and efficiency of the workflow. However, dependencies between tasks must still be defined to maintain the correct execution sequence.
- A. Correct.
Using multiple tasks splits the workflow into smaller, modular units, making it easier to identify and fix issues within specific tasks.
- B. Correct.
Multiple tasks can be configured to run in parallel (when dependencies allow), which optimizes execution time by utilizing resources efficiently.
- C. Incorrect.
This is incorrect because using multiple tasks doesn't inherently increase resource constraints; instead, it provides better control over resource allocation and utilization.
- D. Correct.
Intermediate outputs can be saved and reused between tasks, avoiding the need to recompute data and improving efficiency.
- E. Incorrect.
This is incorrect because defining dependencies between tasks is essential to ensure the correct execution order in multi-task workflows.