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

Databricks-Certified-Data-Engineer-Associate Exam Simulator
  • Exam Code: Databricks-Certified-Data-Engineer-Associate
  • Exam Name: Databricks Certified Data Engineer Associate Exam
  • Updated: Oct 12, 2026
  • Q & A: 322 Questions and Answers

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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
  • 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 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
  • 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 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

In a Lakeflow Declarative Pipeline (Delta Live Tables), a data engineer must ensure that records with a null order_id are discarded from the silver table while the pipeline continues to run.
Which expectation clause should be used?

  • A. CONSTRAINT valid_id EXPECT (order_id IS NOT NULL)
  • B. CONSTRAINT valid_id EXPECT (order_id IS NOT NULL) ON VIOLATION DROP ROW
  • C. CONSTRAINT valid_id EXPECT (order_id IS NOT NULL) ON VIOLATION FAIL UPDATE
  • D. ALTER TABLE silver_orders ADD CONSTRAINT valid_id CHECK (order_id IS NOT NULL)
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Question #2

A new data engineering team team. has been assigned to an ELT project. The new data engineering team will need full privileges on the database customers to fully manage the project.
Which of the following commands can be used to grant full permissions on the database to the new data engineering team?

  • A. GRANT ALL PRIVILEGES ON DATABASE team TO customers;
  • B. GRANT ALL PRIVILEGES ON DATABASE customers TO team;
  • C. GRANT USAGE ON DATABASE customers TO team;
  • D. GRANT SELECT PRIVILEGES ON DATABASE customers TO teams;
  • E. GRANT SELECT CREATE MODIFY USAGE PRIVILEGES ON DATABASE customers TO team;
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Question #3

A developer is building a data pipeline that processes records from a bronze table into a silver table. The bronze table, bronze_events, contains duplicate records due to at-least-once delivery guarantees from the upstream ingestion system. The developer writes the following PySpark code to prepare the data:

The developer notices that after running dropDuplicates() without arguments, some rows that differ only in the event_timestamp column remain. The developer wants to de-duplicate based only on user_id and event_type, keeping one row per unique combination of these two columns.
Which code change achieves this deduplication requirement?

  • A. Replace df.dropDuplicates() with df.dropDuplicates(["user_id", "event_type"])
  • B. Replace df.dropDuplicates() with df.distinct().select("user_id", "event_type")
  • C. Replace df.dropDuplicates() with df.drop("event_timestamp").dropDuplicates()
  • D. Replace df.dropDuplicates() with df.groupBy("user_id", "event_type").count()
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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Question #4

A data engineer needs to incrementally load thousands of new JSON files that land in a cloud storage location every hour. The solution must track which files have already been processed without listing the entire directory each run, and must scale to millions of files over time.
Which approach should the engineer use?

  • A. Use spark.read.json() on the full directory and overwrite the target table each hour.
  • B. Use Auto Loader with cloudFiles.format set to json and a checkpoint location for file state tracking.
  • C. Use a nightly MERGE INTO from a manually maintained list of file paths.
  • D. Use COPY INTO with FORCE = true to reprocess all files each run.
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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Question #5

A data engineer is running a Spark job that processes data stored in Delta format. The engineer wants to reduce the number of files by compacting smaller files into larger ones to improve query performance. Which Delta Lake command performs this operation?

  • A. DELETE
  • B. OPTIMIZE
  • C. VACUUM
  • D. MERGE
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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