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

"Databricks Certified Data Engineer Associate Exam", also known as Databricks-Certified-Data-Engineer-Associate exam, is a Databricks Certification. With the complete collection of questions and answers, Pass4sureCert has assembled to take you through 322 Q&As to your Databricks-Certified-Data-Engineer-Associate Exam preparation. In the Databricks-Certified-Data-Engineer-Associate exam resources, you will cover every field and category in Databricks Certification Certification helping to ready you for your successful Databricks Certification.

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  • Exam Code: Databricks-Certified-Data-Engineer-Associate
  • Exam Name: Databricks Certified Data Engineer Associate Exam
  • Certification Provider: Databricks
  • Corresponding Certification: Databricks Certification
  • Updated: Oct 18, 2026
  • No. of Questions: 322 Questions & Answers with Testing Engine
  • Download Limit: Unlimited

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

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • 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 4
  • 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 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:

A data engineer wants a Structured Streaming job to process all data available in the source, then stop automatically, so it can run on a scheduled job cluster instead of running continuously.
Which trigger should be configured?

  • A. .trigger(continuous="1 second")
  • B. .trigger(once=True) with maxFilesPerTrigger disabled
  • C. .trigger(availableNow=True)
  • D. .trigger(processingTime="0 seconds")
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

Explanation: Only visible for Pass4sureCert members. You can sign-up / login (it's free).

A Data Engineer is building a simple data pipeline using Delta Live Tables (DLT) in Databricks to ingest customer data. The raw customer data is stored in a cloud storage location in JSON format. The task is to create a DLT pipeline that reads the raw JSON data and writes it into a Delta table for further processing. Which code snippet will correctly ingest the raw JSON data and create a Delta table using DLT?

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

Correct Answer: D  🗳️

Explanation: Only visible for Pass4sureCert members. You can sign-up / login (it's free).

A data engineer needs to compute metrics such as:
- rolling 7-day sales
- percent of total revenue
- top-N most profitable categories
Which two approaches are appropriate for implementing these metrics with PySpark DataFrames? (Choose two.)

  • A. Using mapPartitions to manually implement rolling windows.
  • B. Collecting the DataFrame to the driver for faster iterative processing.
  • C. Using window functions for rolling and ranking calculations.
  • D. Using RDD aggregations for better performance.
  • E. Using joins and groupBy to compute percent-of-total metrics.
Reveal Solution  Discussion  0

Correct Answer: C,E  🗳️

Explanation: Only visible for Pass4sureCert members. You can sign-up / login (it's free).

Which two items are characteristics of the Gold Layer? (Choose two.)

  • A. De-normalised
  • B. Historical lineage
  • C. Raw Data
  • D. Read-optimized
  • E. Normalised
Reveal Solution  Discussion  0

Correct Answer: A,D  🗳️

Explanation: Only visible for Pass4sureCert members. You can sign-up / login (it's free).

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: C  🗳️

Explanation: Only visible for Pass4sureCert members. You can sign-up / login (it's free).

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