Databricks Data Engineer Professional exam dumps

Databricks Data Engineer Professional practice question 135 of 313

Databricks Certified Data Engineer Professional. Professional level, Databricks. Free question with the correct answer and a full explanation.

Databricks Data Engineer Professional Question 135

Select 3

A data engineering team is implementing a Lakehouse architecture to manage a Delta table where data is continuously updated and deleted. In the past, they struggled to propagate updates and deletes downstream to analytics systems. How does Change Data Feed (CDF) in Delta Lake address these challenges?

  1. A

    CDF provides a mechanism to track and extract only the rows that were updated, inserted, or deleted in a Delta table.

  2. B

    CDF allows users to rewind to any historical version of the table and extract all changes since that version.

  3. C

    CDF ensures that all downstream systems automatically synchronize with the Delta table without additional processing steps.

  4. D

    CDF eliminates the need for full table scans when identifying changed data.

  5. E

    CDF automatically re-partitions the Delta table to optimize performance after every update or delete.

Show answer and explanation

Correct answers: A, B, D

Explanation

Change Data Feed (CDF) in Delta Lake addresses past challenges in propagating updates and deletes by allowing users to extract precise changes (inserts, updates, and deletes) without scanning the entire table. It also supports historical change tracking and reduces overhead in identifying modified data. However, downstream integration and table optimization tasks still require separate workflows.

  • A. Correct.

    Correct. CDF identifies and extracts only the rows that were changed (inserted, updated, or deleted), addressing the challenge of propagating updates and deletes efficiently.

  • B. Correct.

    Correct. CDF can extract changes from any historical version of the table, making it easier to track and propagate changes over time for downstream systems.

  • C. Incorrect.

    Incorrect. While CDF helps identify changes, it does not automatically synchronize downstream systems. Integration still requires additional steps.

  • D. Correct.

    Correct. By tracking changes at the row level, CDF eliminates the need for full table scans when identifying changed data, saving time and resources.

  • E. Incorrect.

    Incorrect. CDF does not handle table re-partitioning automatically. Re-partitioning, if required, must be performed separately.

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