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DatabricksIntermediateDatabricksData EngineeringApache SparkDelta Lake

Databricks Certified Data Engineer Associate

DATA-ENGINEER-ASSOCIATE

The Databricks Certified Data Engineer Associate exam validates foundational skills using the Databricks Data Intelligence Platform to complete introductory data engineering tasks. Practice with questions covering the Lakehouse architecture, Apache Spark, Delta Lake, ETL pipelines, workflows, and data governance.

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Questions

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  1. 1A data organization leader is upset about the data analysis team’s reports being different from the data engineering team’s reports. The leader believes the siloed nature of their organization’s data engineering and data analysis architectures is to blame. Which of the following describes how a data lakehouse could alleviate this issue?
  2. 2Which of the following describes a scenario in which a data team will want to utilize cluster pools?
  3. 3Which of the following is hosted completely in the control plane of the classic Databricks architecture?
  4. 4Which of the following benefits of using the Databricks Lakehouse Platform is provided by Delta Lake?
  5. 5Which of the following describes the storage organization of a Delta table?
  6. 6Which of the following code blocks will remove the rows where the value in column age is greater than 25 from the existing Delta table my_table and save the updated table?
  7. 7A data engineer has realized that they made a mistake when making a daily update to a table. They need to use Delta time travel to restore the table to a version that is 3 days old. However, when the data engineer attempts to time travel to the older version, they are unable to restore the data because the data files have been deleted. Which of the following explains why the data files are no longer present?
  8. 8Which of the following Git operations must be performed outside of Databricks Repos?
  9. 9Which of the following data lakehouse features results in improved data quality over a traditional data lake?
  10. 10A data engineer needs to determine whether to use the built-in Databricks Notebooks versioning or version their project using Databricks Repos. Which of the following is an advantage of using Databricks Repos over the Databricks Notebooks versioning?