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Databricks Databricks-Certified-Data-Engineer-Associate Exam With Confidence Using Practice Dumps

Exam Code:
Databricks-Certified-Data-Engineer-Associate
Exam Name:
Databricks Certified Data Engineer Associate Exam
Certification:
Databricks Certification
Vendor:
Databricks
Questions:
153
Last Updated:
Feb 8, 2026
Exam Status:
Stable
Databricks Databricks-Certified-Data-Engineer-Associate

Databricks-Certified-Data-Engineer-Associate: Databricks Certification Exam 2025 Study Guide Pdf and Test Engine

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Databricks Certified Data Engineer Associate Exam Questions and Answers

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Question 1

A data engineer is working on a Databricks project that utilizes cloud storage. The data engineer wants to load several json files from containers on a storage account as soon as the file arrives within the storage account.

Which syntax should the data engineer follow to first load the files into a dataframe and check that it is working as expected using Python?

Options:

A.

df = spark.readStream.format("json").load("input/path")

B.

df = spark.readStream.format("cloud"),option("json").load("/input/path")

C.

df = spark.readStream.format("cloudFiles") .option("cloudFiles.format", "json") .load("/input/path")

D.

df = spark.read.json("inp i./path")

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Question 2

What is the structure of an Asset Bundle?

Options:

A.

A single plain text file enumerating the names of assets to be migrated to a new workspace.

B.

A compressed archive (ZIP) that solely contains workspace assets without any accompanying metadata.

C.

A YAML configuration file that specifies the artifacts, resources, and configurations for the project.

D.

A Docker image containing runtime environments and the source code of the assets

Question 3

A global retail company sells products across multiple categories (e.g.. Electronics, Clothing) and regions (e.g.. North. South, East. West). The sales team has provided the data engineer with a PySpark dataframe named sales_df as below and the team wants the data engineer to analyze the sales data to help them make strategic decisions.

Options:

A.

Category_sales = sales df.groupBy("category").agg(sum("sales amount") .alias ("total sales amount"))

B.

Category_sales = sales_df.sum("3ales_amount"). g-1- upBy("categcryn).alias("toLal_sales_amount))

C.

Category_sale: .es df -agg (sum ("sales amount") .-;r*i:rRy ("category") .alias ("total sa.en amount"))

D.

Category_sales = sales_df.groupBy("reqion"). agq(sum("sales_amountn).alias(ntotal_sales_amount''))

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