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

  • Exam Code: Databricks-Certified-Data-Engineer-Associate
  • Exam Name: Databricks Certified Data Engineer Associate Exam
  • Updated: Nov 06, 2026     Q & A: 322 Questions and Answers

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

TopicDetails
Topic 1
  • 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.
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
  • 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
  • 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.

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 access the view created by the sales team, using a shared cluster. The data engineer has been provided usage permissions on the catalog and schema. In order to access the view created by sales team. What are the minimum permissions the data engineer would require in addition?

  • A. Needs SELECT permission only on the VIEW
  • B. Needs ALL PRIVILEGES on the VIEW
  • C. Needs SELECT permission on the VIEW and the underlying TABLE.
  • D. Needs ALL PRIVILEGES at the SCHEMA level
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

Question #2

Which of the following can be used to simplify and unify siloed data architectures that are specialized for specific use cases?

  • A. Data warehouse
  • B. Data lake
  • C. None of these
  • D. Data lakehouse
  • E. All of these
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

Question #3

A data engineer has created a new database using the following command:
CREATE DATABASE IF NOT EXISTS customer360;
In which of the following locations will the customer360 database be located?

  • A. dbfs:/user/hive/warehouse
  • B. dbfs:/user/hive/database/customer360
  • C. dbfs:/user/hive/customer360
  • D. More information is needed to determine the correct response
  • E. dbfs:/user/hive/database
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

Question #4

A dataset has been defined using Delta Live Tables and includes an expectations clause:
CONSTRAINT valid_timestamp EXPECT (timestamp > '2020-01-01') ON
VIOLATION FAIL UPDATE
What is the expected behavior when a batch of data containing data that violates these constraints is processed?

  • A. Records that violate the expectation cause the job to fail.
  • B. Records that violate the expectation are added to the target dataset and recorded as invalid in the event log.
  • C. Records that violate the expectation are dropped from the target dataset and recorded as invalid in the event log.
  • D. Records that violate the expectation are dropped from the target dataset and loaded into a quarantine table.
  • E. Records that violate the expectation are added to the target dataset and flagged as invalid in a field added to the target dataset.
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

Question #5

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  🗳️

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