Databricks Data Engineer Associate Question 251
Single answerYou are working on a Databricks project where multiple teams need access to the same large dataset stored in an Azure Data Lake. The dataset is updated frequently by an external application, but you do not want to copy the data into your Databricks workspace to avoid storage duplication. Which of the following scenarios would justify using an external table?
- A
To allow multiple teams to query shared data stored in an external location without duplicating it.
- B
To enable data versioning and rollback within the Databricks-managed Delta Lake storage.
- C
To simplify schema enforcement and optimize performance by moving the data into Databricks-managed storage.
- D
To ensure that the data is fully managed and secured within the Databricks ecosystem.
Show answer and explanation
Correct answer: A
Explanation
External tables are ideal for scenarios where the data resides in an external system (e.g., Azure Data Lake, S3) and multiple teams need to query the shared data without duplicating or moving it. This setup avoids storage redundancy and enables efficient data access while keeping the data in its original location.
- A. Correct.
This is correct. External tables allow you to query data stored in external systems (like Azure Data Lake) without duplicating or moving it into Databricks-managed storage.
- B. Incorrect.
This is incorrect. Data versioning and rollback are features of Delta Lake, but they are not specific to external tables.
- C. Incorrect.
This is incorrect. Moving data into Databricks-managed storage contradicts the use of external tables, which are designed to work with data in external locations.
- D. Incorrect.
This is incorrect. External tables do not ensure data is fully managed within Databricks, as the data resides outside of the Databricks ecosystem.