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

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 06, 2026

Q & A: 135 Questions and Answers

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

1. A developer needs to produce a Python dictionary using data stored in a small Parquet table, which looks like this:

The resulting Python dictionary must contain a mapping of region -> region id containing the smallest 3 region_id values.
Which code fragment meets the requirements?
A)

B)

C)

D)

The resulting Python dictionary must contain a mapping of region -> region_id for the smallest 3 region_id values.
Which code fragment meets the requirements?

A) regions = dict(
regions_df
.select('region_id', 'region')
.limit(3)
.collect()
)
B) regions = dict(
regions_df
.select('region_id', 'region')
.sort('region_id')
.take(3)
)
C) regions = dict(
regions_df
.select('region', 'region_id')
.sort(desc('region_id'))
.take(3)
)
D) regions = dict(
regions_df
.select('region', 'region_id')
.sort('region_id')
.take(3)
)


2. A data engineer is running a Spark job to process a dataset of 1 TB stored in distributed storage. The cluster has 10 nodes, each with 16 CPUs. Spark UI shows:
Low number of Active Tasks
Many tasks complete in milliseconds
Fewer tasks than available CPUs
Which approach should be used to adjust the partitioning for optimal resource allocation?

A) Set the number of partitions by dividing the dataset size (1 TB) by a reasonable partition size, such as 128 MB
B) Set the number of partitions equal to the total number of CPUs in the cluster
C) Set the number of partitions equal to the number of nodes in the cluster
D) Set the number of partitions to a fixed value, such as 200


3. A developer initializes a SparkSession:

spark = SparkSession.builder \
.appName("Analytics Application") \
.getOrCreate()
Which statement describes the spark SparkSession?

A) A SparkSession is unique for each appName, and calling getOrCreate() with the same name will return an existing SparkSession once it has been created.
B) A new SparkSession is created every time the getOrCreate() method is invoked.
C) The getOrCreate() method explicitly destroys any existing SparkSession and creates a new one.
D) If a SparkSession already exists, this code will return the existing session instead of creating a new one.


4. 24 of 55.
Which code should be used to display the schema of the Parquet file stored in the location events.parquet?

A) spark.sql("SELECT schema FROM events.parquet").show()
B) spark.sql("SELECT * FROM events.parquet").show()
C) spark.read.parquet("events.parquet").printSchema()
D) spark.read.format("parquet").load("events.parquet").show()


5. A data engineer replaces the exact percentile() function with approx_percentile() to improve performance, but the results are drifting too far from expected values.
Which change should be made to solve the issue?

A) Increase the last value of the percentage parameter to increase the accuracy of the percentile ranges
B) Increase the value of the accuracy parameter in order to increase the memory usage but also improve the accuracy
C) Decrease the first value of the percentage parameter to increase the accuracy of the percentile ranges
D) Decrease the value of the accuracy parameter in order to decrease the memory usage but also improve the accuracy


Solutions:

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

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