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Free Databricks Databricks-Certified-Professional-Data-Engineer Exam Questions

Absolute Free Databricks-Certified-Professional-Data-Engineer Exam Practice for Comprehensive Preparation 

  • Databricks Databricks-Certified-Professional-Data-Engineer Exam Questions
  • Provided By: Databricks
  • Exam: Databricks Certified Professional Data Engineer
  • Certification: Databricks Certified Professional
  • Total Questions: 247
  • Updated On: Mar 28, 2025
  • Rated: 4.9 |
  • Online Users: 494
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  • Question 1
    • A junior data engineer seeks to leverage Delta Lake's Change Data Feed functionality to create a Type 1 table representing all of the values that have ever been valid for all rows in a bronze table created with the property delta.enableChangeDataFeed = true. They plan to execute the following code as a daily job:
      Databricks-Certified-Professional-Data-Engineer-page61-image24
      Which statement describes the execution and results of running the above query multiple times?

      Answer: B
  • Question 2
    • Which statement describes Delta Lake optimized writes?

      Answer: E
  • Question 3

    • A Databricks job has been configured with 3 tasks, each of which is a Databricks notebook. Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a serial dependency on Task A. If task A fails during a scheduled run, which statement describes the results of this run? 


      Answer: D
  • Question 4
    • The data engineering team has a singleplex bronze table called ‘orders_raw’ where new orders data is appended every night. They created a new Silver table called ‘orders_cleaned’ in order to provide a more refined view of the orders data.

      The team wants to create a batch processing pipeline to process all new records inserted in the orders_raw table and propagate them to the orders_cleaned table.

      Which solution minimizes the compute costs to propagate this batch of data?


      Answer: B
  • Question 5
    • A DLT pipeline includes the following streaming tables:

      • raw_iot ingests raw device measurement data from a heart rate tracking device.

      • bpm_stats incrementally computes user statistics based on BPM measurements from raw_iot.

      How can the data engineer configure this pipeline to be able to retain manually deleted or updated records in the raw_iot table, while recomputing the downstream table bpm_stats table when a pipeline update is run?


      Answer: B
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