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Databricks Databricks-Certified-Associate-Developer-for-Apache-Spark-3.5 Exam With Confidence Using Practice Dumps

Exam Code:
Databricks-Certified-Associate-Developer-for-Apache-Spark-3.5
Exam Name:
Databricks Certified Associate Developer for Apache Spark 3.5 – Python
Certification:
Databricks Certification
Vendor:
Databricks
Questions:
136
Last Updated:
Sep 17, 2026
Exam Status:
Stable
Databricks Databricks-Certified-Associate-Developer-for-Apache-Spark-3.5

Databricks-Certified-Associate-Developer-for-Apache-Spark-3.5: Databricks Certification Exam 2025 Study Guide Pdf and Test Engine

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Databricks Certified Associate Developer for Apache Spark 3.5 – Python Questions and Answers

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Question 1

What is the relationship between jobs, stages, and tasks during execution in Apache Spark?

Options:

Options:

A.

A job contains multiple stages, and each stage contains multiple tasks.

B.

A job contains multiple tasks, and each task contains multiple stages.

C.

A stage contains multiple jobs, and each job contains multiple tasks.

D.

A stage contains multiple tasks, and each task contains multiple jobs.

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Question 2

30 of 55.

A data engineer is working on a num_df DataFrame and has a Python UDF defined as:

def cube_func(val):

return val * val * val

Which code fragment registers and uses this UDF as a Spark SQL function to work with the DataFrame num_df?

Options:

A.

spark.udf.register("cube_func", cube_func)

num_df.selectExpr("cube_func(num)").show()

B.

num_df.select(cube_func("num")).show()

C.

spark.createDataFrame(cube_func("num")).show()

D.

num_df.register("cube_func").select("num").show()

Question 3

47 of 55.

A data engineer has written the following code to join two DataFrames df1 and df2:

df1 = spark.read.csv("sales_data.csv")

df2 = spark.read.csv("product_data.csv")

df_joined = df1.join(df2, df1.product_id == df2.product_id)

The DataFrame df1 contains ~10 GB of sales data, and df2 contains ~8 MB of product data.

Which join strategy will Spark use?

Options:

A.

Shuffle join, as the size difference between df1 and df2 is too large for a broadcast join to work efficiently.

B.

Shuffle join, because AQE is not enabled, and Spark uses a static query plan.

C.

Shuffle join because no broadcast hints were provided.

D.

Broadcast join, as df2 is smaller than the default broadcast threshold.

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