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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:
Vendor:
Questions:
153
Last Updated:
Feb 6, 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

Question 1

An engineering manager uses a Databricks SQL query to monitor ingestion latency for each data source. The manager checks the results of the query every day, but they are manually rerunning the query each day and waiting for the results.

Which of the following approaches can the manager use to ensure the results of the query are updated each day?

Options:

A.

They can schedule the query to refresh every 1 day from the SQL endpoint's page in Databricks SQL.

B.

They can schedule the query to refresh every 12 hours from the SQL endpoint's page in Databricks SQL.

C.

They can schedule the query to refresh every 1 day from the query's page in Databricks SQL.

D.

They can schedule the query to run every 1 day from the Jobs UI.

E.

They can schedule the query to run every 12 hours from the Jobs UI.

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Question 2

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''))

Question 3

A data engineer is designing a data pipeline. The source system generates files in a shared directory that is also used by other processes. As a result, the files should be kept as is and will accumulate in the directory. The data engineer needs to identify which files are new since the previous run in the pipeline, and set up the pipeline to only ingest those new files with each run.

Which of the following tools can the data engineer use to solve this problem?

Options:

A.

Unity Catalog

B.

Delta Lake

C.

Databricks SQL

D.

Data Explorer

E.

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