Databricks Data Engineer Associate Question 424
Select 4A data engineering team is designing a Databricks Job to process data in multiple stages: first, a data ingestion step, followed by a data transformation step, and finally a machine learning model training step. What are the benefits of using multiple tasks in this Databricks Job?
- A
Improved modularity and maintainability of the workflow
- B
Ability to retry individual tasks without restarting the entire Job
- C
Reduced overall cost of running the Job by combining all steps into a single task
- D
Faster execution by enabling parallelism between independent tasks
- E
Simplified debugging by isolating errors to specific tasks
Show answer and explanation
Correct answers: A, B, D, E
Explanation
Using multiple tasks in Databricks Jobs offers several key benefits: it improves modularity, enables task-level retries, supports parallelism for faster execution, and simplifies debugging by isolating errors to specific tasks. However, combining all steps into a single task would reduce these benefits and may lead to inefficiencies.
- A. Correct.
Correct: Using multiple tasks in a Job allows you to logically separate the workflow into modular stages, making the Job easier to maintain and update over time.
- B. Correct.
Correct: With multiple tasks, you can retry individual failed tasks without having to restart the entire Job, saving time and resources.
- C. Incorrect.
Incorrect: Combining all steps into a single task would not reduce cost, on the contrary, it may increase costs due to inefficiencies and lack of error isolation.
- D. Correct.
Correct: Multiple tasks can execute in parallel if they are independent, which can lead to faster overall execution of the Job.
- E. Correct.
Correct: Debugging is simplified because errors can be traced back to specific tasks, making it easier to identify and address issues.