Associate-Developer-Apache-Spark-3.5 exam dumps

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  • Exam Code: Associate-Developer-Apache-Spark-3.5
  • Exam Name: Databricks Certified Associate Developer for Apache Spark 3.5 - Python
  • No. of Questions: 135 Questions and Answers
  • Updated: Sep 12, 2026

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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:

SectionObjectives
Data Ingestion and Storage- Delta Lake basics
- Reading and writing data (Parquet, JSON, CSV)
Apache Spark Fundamentals- RDD vs DataFrame vs Dataset concepts
- Spark architecture and execution model
Spark SQL- SQL queries on DataFrames and tables
- Window functions and aggregations
Structured Streaming Basics- Streaming DataFrames
- Windowed aggregations in streaming
DataFrame API with PySpark- Transformations and actions
- DataFrame creation and schema management
- Built-in functions and expressions
Data Processing and Performance- Optimization techniques
- Joins and data partitioning
- Caching and persistence strategies

Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:

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?

  • A. spark.createDataFrame(cube_func("num")).show()
  • B. num_df.register("cube_func").select("num").show()
  • C. num_df.select(cube_func("num")).show()
  • D. spark.udf.register("cube_func", cube_func)
    num_df.selectExpr("cube_func(num)").show()
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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An engineer wants to join two DataFrames df1 and df2 on the respective employee_id and emp_id columns:
df1: employee_id INT, name STRING
df2: emp_id INT, department STRING
The engineer uses:
result = df1.join(df2, df1.employee_id == df2.emp_id, how='inner')
What is the behaviour of the code snippet?

  • A. The code fails to execute because PySpark does not support joining DataFrames with a different structure
  • B. The code works as expected because the join condition explicitly matches employee_id from df1 with emp_id from df2
  • C. The code fails to execute because the column names employee_id and emp_id do not match automatically
  • D. The code fails to execute because it must use on='employee_id' to specify the join column explicitly
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Explanation: Only visible for PassTestking members. You can sign-up / login (it's free).

A data engineer is reviewing a Spark application that applies several transformations to a DataFrame but notices that the job does not start executing immediately.
Which two characteristics of Apache Spark's execution model explain this behavior?
Choose 2 answers:

  • A. Transformations are executed immediately to build the lineage graph.
  • B. The Spark engine optimizes the execution plan during the transformations, causing delays.
  • C. Only actions trigger the execution of the transformation pipeline.
  • D. The Spark engine requires manual intervention to start executing transformations.
  • E. Transformations are evaluated lazily.
Reveal Solution  Discussion  0

Correct Answer: C,E  🗳️

Explanation: Only visible for PassTestking members. You can sign-up / login (it's free).

35 of 55.
A data engineer is building a Structured Streaming pipeline and wants it to recover from failures or intentional shutdowns by continuing where it left off.
How can this be achieved?

  • A. By configuring the option checkpointLocation during writeStream.
  • B. By configuring the option recoveryLocation during SparkSession initialization.
  • C. By configuring the option checkpointLocation during readStream.
  • D. By configuring the option recoveryLocation during writeStream.
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

Explanation: Only visible for PassTestking members. You can sign-up / login (it's free).

13 of 55.
A developer needs to produce a Python dictionary using data stored in a small Parquet table, which looks like this:
region_id
region_name
10
North
12
East
14
West
The resulting Python dictionary must contain a mapping of region_id to region_name, containing the smallest 3 region_id values.
Which code fragment meets the requirements?

  • A. regions_dict = regions.select("region_id", "region_name").take(3)
  • B. regions_dict = dict(regions.orderBy("region_id").limit(3).rdd.map(lambda x: (x.region_id, x.region_name)).collect())
  • C. regions_dict = dict(regions.select("region_id", "region_name").rdd.collect())
  • D. regions_dict = dict(regions.take(3))
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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