Databricks Associate-Developer-Apache-Spark-3.5 exam dumps : 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 02, 2026     Q & A: 135 Questions and Answers

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

SectionWeightObjectives
Using Spark SQL20%- Spark SQL Operations
  • 1. Aggregations and grouping
  • 2. Built-in SQL functions
  • 3. Joins and subqueries
  • 4. Window functions
  • 5. Filtering and sorting data
Developing Apache Spark DataFrame API Applications30%- DataFrame Operations
  • 1. User Defined Functions
  • 2. Selecting and renaming columns
  • 3. Creating and transforming DataFrames
  • 4. Handling null values
  • 5. Reading and writing data
  • 6. Partitioning data
  • 7. Working with complex data types
Troubleshooting and Tuning10%- Performance Optimization
  • 1. Caching and persistence
  • 2. Shuffle optimization
  • 3. Broadcast joins
  • 4. Execution plan analysis
Apache Spark Architecture and Components20%- Spark Architecture
  • 1. Driver and Executor roles
  • 2. Adaptive Query Execution
  • 3. Cluster managers
  • 4. Lazy evaluation
Using Pandas API on Spark5%- Pandas API
  • 1. Pandas on Spark DataFrames
  • 2. Pandas transformations
  • 3. Interoperability with PySpark
Using Spark Connect to Deploy Applications5%- Spark Connect
  • 1. Application deployment
  • 2. Remote Spark sessions
  • 3. Client-server architecture
Structured Streaming10%- Streaming Applications
  • 1. Streaming sources and sinks
  • 2. Triggers and checkpoints
  • 3. Structured Streaming concepts
  • 4. Output modes

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

Question #1

Given a CSV file with the content:

And the following code:
from pyspark.sql.types import *
schema = StructType([
StructField("name", StringType()),
StructField("age", IntegerType())
])
spark.read.schema(schema).csv(path).collect()
What is the resulting output?

  • A. [Row(name='alladin', age=20)]
  • B. [Row(name='bambi'), Row(name='alladin', age=20)]
  • C. The code throws an error due to a schema mismatch.
  • D. [Row(name='bambi', age=None), Row(name='alladin', age=20)]
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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Question #2

A data engineer is working on the DataFrame:

(Referring to the table image: it has columns Id, Name, count, and timestamp.) Which code fragment should the engineer use to extract the unique values in the Name column into an alphabetically ordered list?

  • A. df.select("Name").distinct().orderBy(df["Name"].desc())
  • B. df.select("Name").distinct().orderBy(df["Name"])
  • C. df.select("Name").distinct()
  • D. df.select("Name").orderBy(df["Name"].asc())
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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Question #3

2 of 55. Which command overwrites an existing JSON file when writing a DataFrame?

  • A. df.write.option("overwrite").json("path/to/file")
  • B. df.write.mode("append").json("path/to/file")
  • C. df.write.mode("overwrite").json("path/to/file")
  • D. df.write.json("path/to/file")
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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Question #4

43 of 55.
An organization has been running a Spark application in production and is considering disabling the Spark History Server to reduce resource usage.
What will be the impact of disabling the Spark History Server in production?

  • A. Improved job execution speed due to reduced logging overhead
  • B. Prevention of driver log accumulation during long-running jobs
  • C. Loss of access to past job logs and reduced debugging capability for completed jobs
  • D. Enhanced executor performance due to reduced log size
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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Question #5

A Spark application developer wants to identify which operations cause shuffling, leading to a new stage in the Spark execution plan.
Which operation results in a shuffle and a new stage?

  • A. DataFrame.withColumn()
  • B. DataFrame.filter()
  • C. DataFrame.select()
  • D. DataFrame.groupBy().agg()
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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