Databricks Certified Associate Developer for Apache Spark 3.5 - Python : Associate-Developer-Apache-Spark-3.5 valid dumps

Associate-Developer-Apache-Spark-3.5 real exams

Exam Code: Associate-Developer-Apache-Spark-3.5

Exam Name: Databricks Certified Associate Developer for Apache Spark 3.5 - Python

Updated: Jun 03, 2026

Q & A: 135 Questions and Answers

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

1. 45 of 55.
Which feature of Spark Connect should be considered when designing an application that plans to enable remote interaction with a Spark cluster?

A) It can be used to interact with any remote cluster using the REST API.
B) It is primarily used for data ingestion into Spark from external sources.
C) It allows for remote execution of Spark jobs.
D) It provides a way to run Spark applications remotely in any programming language.


2. 49 of 55.
In the code block below, aggDF contains aggregations on a streaming DataFrame:
aggDF.writeStream \
.format("console") \
.outputMode("???") \
.start()
Which output mode at line 3 ensures that the entire result table is written to the console during each trigger execution?

A) COMPLETE
B) REPLACE
C) APPEND
D) AGGREGATE


3. 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) num_df.select(cube_func("num")).show()
B) num_df.register("cube_func").select("num").show()
C) spark.createDataFrame(cube_func("num")).show()
D) spark.udf.register("cube_func", cube_func)
num_df.selectExpr("cube_func(num)").show()


4. A data engineer is running a batch processing job on a Spark cluster with the following configuration:
10 worker nodes
16 CPU cores per worker node
64 GB RAM per node
The data engineer wants to allocate four executors per node, each executor using four cores.
What is the total number of CPU cores used by the application?

A) 40
B) 80
C) 160
D) 64


5. 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.


Solutions:

Question # 1
Answer: C
Question # 2
Answer: A
Question # 3
Answer: D
Question # 4
Answer: B
Question # 5
Answer: A

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