Databricks Data Engineer Associate Question 426
Select 3You are tasked with designing a Databricks Job to process a large dataset in multiple stages: data ingestion, data transformation, and model training. To optimize the workflow, you decide to use multiple tasks within the Job instead of a single task. What are the key benefits of using multiple tasks in this scenario?
- A
Improved modularity and reusability of different stages in the workflow
- B
Reduced cost by running all stages of the workflow in a single task cluster
- C
Improved error isolation and debugging by separating stages into distinct tasks
- D
Enhanced flexibility in specifying dependencies between different stages
- E
Guaranteed faster execution of the entire workflow compared to using a single task
Show answer and explanation
Correct answers: A, C, D
Explanation
Using multiple tasks in Databricks Jobs enables modularity, error isolation, and dependency management. These features enhance the maintainability and flexibility of workflows, especially in complex pipelines with multiple stages. However, performance and cost optimizations depend on how tasks and clusters are configured, not solely on the use of multiple tasks.
- A. Correct.
Using multiple tasks allows each stage of the workflow to be modular and reusable. For example, the data ingestion task can be reused in other jobs without needing to rebuild it.
- B. Incorrect.
Running all stages in a single task cluster may not reduce costs; in fact, separating tasks into distinct clusters might optimize resource allocation and reduce runtime costs.
- C. Correct.
By separating stages into distinct tasks, errors in one stage (e.g., data transformation) do not affect other stages (e.g., model training). This makes debugging more straightforward.
- D. Correct.
Multiple tasks provide the ability to define dependencies between stages, such as ensuring data transformation is complete before model training begins. This flexibility is a key benefit.
- E. Incorrect.
Using multiple tasks does not inherently guarantee faster execution. Performance depends on the computational resources and the complexity of each task.