Databricks Certified Data Engineer Associate : Databricks-Certified-Data-Engineer-Associate

  • Exam Code: Databricks-Certified-Data-Engineer-Associate
  • Exam Name: Databricks Certified Data Engineer Associate Exam
  • Updated: Oct 11, 2026
  • Q & A: 322 Questions and Answers

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About Databricks Certified Data Engineer Associate : Databricks-Certified-Data-Engineer-Associate Exam

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Databricks Databricks-Certified-Data-Engineer-Associate Exam Syllabus Topics:

TopicDetails
Topic 1
  • Production Pipelines: It focuses on identifying the advantages of using multiple tasks in Jobs, a suitable scenario where predecessor task should be set up, CRON as an opportunity for scheduling opportunity, and how an alert can be sent via email. The topic also discusses setting up a predecessor task in Jobs, reviewing a task's execution history, and debugging a failed task. Lastly, it delves into setting up a retry policy in case of failure and creating an alert in the case of a failed task.
Topic 2
  • Data Governance: It identifies one of the four areas of data governance, Unity Catalog securables, and the cluster security modes. It also discusses how to create a UC-enabled all-purpose cluster and a DBSQL warehouse. The topic explains how to implement data object access control, create a DBSQL warehouse, and e a UC-enabled all-purpose cluster.
Topic 3
  • ELT with Apache Spark: It focuses on extracting data, identifying the prefix, creating a view, duplicating rows, creating a new table, utilizing the dot, parsing JSON, and defining a SQL UDF. Moreover, the topic delves into describing the security model, identifying the location of a function, and identifying the PIVOT.
Topic 4
  • Incremental Data Processing: In this topic questions about identifying Delta Lake, benefits of ACID transactions, a scenario to use an external table, location of a table, the benefits of Zordering, the kind of files, CTAS as a solution, the impact of ON VIOLATION DROP ROW and ON VIOLATION FAIL UPDATE, and the necessary component to create a new DLT pipeline. Moreover, the topic also discusses directory structure of Delta Lake files, generated column, adding a table comment, and the benefits of the MERGE command.
Topic 5
  • Databricks Lakehouse Platform: This topic covers the relationship between the data lakehouse and the data warehouse, the improvement in data quality, comparing and contrasting silver and gold tables, elements of the Databricks Platform Architecture, and differentiating between all-purpose clusters and jobs clusters. Moreover, it identifies how cluster software is versioned, how clusters can be filtered, how to use multiple languages, how to run one notebook, how notebooks can be shared, Git operations, and limitations in Databricks Notebooks. Lastly, the topic describes how clusters are terminated, how to use multiple languages, and how Databricks Repos enables CI
  • CD workflows.

Reference: https://www.databricks.com/learn/certification/data-engineer-associate

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Databricks Certified Data Engineer Associate Sample Questions:

Question #1

A data engineer needs to use a Delta table as part of a data pipeline, but they do not know if they have the appropriate permissions.
In which of the following locations can the data engineer review their permissions on the table?

  • A. Jobs
  • B. Databricks Filesystem
  • C. Data Explorer
  • D. Repos
  • E. Dashboards
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

Question #2

A data engineer needs to append new records to an existing Delta table while preserving existing data. The ingestion pipeline runs every hour and adds incremental data without replacing previous records. Which write mode should be used?

  • A. overwrite
  • B. ignore
  • C. errorIfExists
  • D. append
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

Question #3

A data engineering team is analyzing customer transactions using a PySpark DataFrame named transactions with columns customer_id, amount, and category.
The team needs to compute, for each customer, the total transaction amount, the average transaction amount, and the maximum amount - all in a single operation for efficiency.
Which code fragment should be used to achieve this in PySpark?

  • A.
  • B.
  • C.
  • D.
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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Question #4

A data engineer needs to apply custom logic to identify employees with more than 5 years of experience in array column employees in table stores. The custom logic should create a new column exp_employees that is an array of all of the employees with more than 5 years of experience for each row. In order to apply this custom logic at scale, the data engineer wants to use the FILTER higher-order function.
Which of the following code blocks successfully completes this task?

  • A.
  • B.
  • C.
  • D.
  • E.
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

Question #5

A data engineering team wants to validate a new ingestion pipeline locally while ensuring large aggregations run in serverless compute in their Databricks workspace. They plan to use Databricks Connect and have the option to attach to either a shared cluster or serverless.
Which workspace requirement should be confirmed first to avoid connection failures?

  • A. Verify that the local Spark version equals the serverless Spark version to satisfy Spark Connect parity.
  • B. Verify that the workspace has Unity Catalog enabled and that the Databricks Connect version supports serverless for the target Runtime release.
  • C. Verify that only assigned access mode clusters are used because serverless is not supported by Databricks Connect.
  • D. Verify that the workspace has Unity Catalog disabled and the Databricks Connect version is less than the serverless Runtime version.
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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