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

  • Exam Code: Associate-Developer-Apache-Spark-3.5
  • Exam Name: Databricks Certified Associate Developer for Apache Spark 3.5 - Python
  • Updated: Oct 04, 2026
  • Q & A: 135 Questions and Answers

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

SectionObjectives
Topic 1: Structured Streaming Basics- Streaming DataFrames
- Windowed aggregations in streaming
Topic 2: Data Processing and Performance- Optimization techniques
- Joins and data partitioning
- Caching and persistence strategies
Topic 3: Data Ingestion and Storage- Delta Lake basics
- Reading and writing data (Parquet, JSON, CSV)
Topic 4: Spark SQL- Window functions and aggregations
- SQL queries on DataFrames and tables
Topic 5: DataFrame API with PySpark- Built-in functions and expressions
- DataFrame creation and schema management
- Transformations and actions
Topic 6: Apache Spark Fundamentals- RDD vs DataFrame vs Dataset concepts
- Spark architecture and execution model

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

Question #1

Given a DataFrame df that has 10 partitions, after running the code:
result = df.coalesce(20)
How many partitions will the result DataFrame have?

  • A. 20
  • B. 10
  • C. 1
  • D. Same number as the cluster executors
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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Question #2

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. Decrease the first value of the percentage parameter to increase the accuracy of the percentile ranges
  • B. Increase the last value of the percentage parameter to increase the accuracy of the percentile ranges
  • C. Increase the value of the accuracy parameter in order to increase the memory usage but also improve the accuracy
  • D. Decrease the value of the accuracy parameter in order to decrease the memory usage but also improve the accuracy
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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Question #3

A data analyst builds a Spark application to analyze finance data and performs the following operations: filter, select, groupBy, and coalesce.
Which operation results in a shuffle?

  • A. coalesce
  • B. filter
  • C. select
  • D. groupBy
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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Question #4

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

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

Correct Answer: B  🗳️

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Question #5

15 of 55.
A data engineer is working on a Streaming DataFrame (streaming_df) with the following streaming data:
id
name
count
timestamp
1
Delhi
20
2024-09-19T10:11
1
Delhi
50
2024-09-19T10:12
2
London
50
2024-09-19T10:15
3
Paris
30
2024-09-19T10:18
3
Paris
20
2024-09-19T10:20
4
Washington
10
2024-09-19T10:22
Which operation is supported with streaming_df?

  • A. streaming_df.show()
  • B. streaming_df.select(countDistinct("name"))
  • C. streaming_df.count()
  • D. streaming_df.filter("count < 30")
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

Explanation: Only visible for Exams4Collection members. You can sign-up / login (it's free).

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