Databricks Data Engineer Associate Question 369
Select 3You are designing a Delta Live Tables (DLT) pipeline to process data for a real-time analytics application. As part of the design, you need to configure the pipeline's 'target' and include specific notebook libraries. What are the primary purposes of configuring the 'target' and using notebook libraries in a DLT pipeline?
- A
The 'target' specifies the location where the transformed data will be stored.
- B
Notebook libraries are used to specify the pipeline's cluster configuration.
- C
Notebook libraries allow the inclusion of custom transformations and reusable logic in the pipeline.
- D
The 'target' ensures the pipeline can handle schema evolution automatically.
- E
Notebook libraries allow the pipeline to interact with external APIs or custom code during execution.
Show answer and explanation
Correct answers: A, C, E
Explanation
In Delta Live Tables pipelines, the 'target' parameter is critical for defining where the output data and tables will be stored, ensuring the pipeline's results are available for downstream use. Notebook libraries, on the other hand, provide flexibility by allowing custom code and external functionalities to be incorporated into the pipeline, enabling more complex transformations or integrations with external systems.
- A. Correct.
Correct: The 'target' parameter defines the location (e.g., a database or storage path) where the resulting tables and data from the pipeline will be stored.
- B. Incorrect.
Incorrect: Notebook libraries are not used for cluster configuration. Cluster configurations are typically defined separately in pipeline settings or cluster definitions.
- C. Correct.
Correct: Notebook libraries enable the inclusion of custom code, functions, and reusable logic for data processing within the pipeline.
- D. Incorrect.
Incorrect: While Delta Live Tables supports schema evolution, this is not directly related to the 'target' parameter. Schema evolution settings are configured separately.
- E. Correct.
Correct: Notebook libraries can include external dependencies or logic (e.g., API calls or custom Python code) that the pipeline may need during execution.