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

Associate-Developer-Apache-Spark-3.5

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

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

Updated: Aug 17, 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: Apache Spark Fundamentals- Spark architecture and execution model
- RDD vs DataFrame vs Dataset concepts
Topic 3: Spark SQL- Window functions and aggregations
- SQL queries on DataFrames and tables
Topic 4: Data Processing and Performance- Joins and data partitioning
- Optimization techniques
- Caching and persistence strategies
Topic 5: DataFrame API with PySpark- Transformations and actions
- Built-in functions and expressions
- DataFrame creation and schema management
Topic 6: Data Ingestion and Storage- Delta Lake basics
- Reading and writing data (Parquet, JSON, CSV)

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

1. A DataFrame df has columns name, age, and salary. The developer needs to sort the DataFrame by age in ascending order and salary in descending order.
Which code snippet meets the requirement of the developer?

A) df.orderBy("age", "salary", ascending=[True, False]).show()
B) df.orderBy(col("age").asc(), col("salary").asc()).show()
C) df.sort("age", "salary", ascending=[False, True]).show()
D) df.sort("age", "salary", ascending=[True, True]).show()


2. 24 of 55.
Which code should be used to display the schema of the Parquet file stored in the location events.parquet?

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


3. What is the relationship between jobs, stages, and tasks during execution in Apache Spark?
Options:

A) A stage contains multiple jobs, and each job contains multiple tasks.
B) A job contains multiple stages, and each stage contains multiple tasks.
C) A job contains multiple tasks, and each task contains multiple stages.
D) A stage contains multiple tasks, and each task contains multiple jobs.


4. 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) 1
B) Same number as the cluster executors
C) 10
D) 20


5. Given the following code snippet in my_spark_app.py:

What is the role of the driver node?

A) The driver node orchestrates the execution by transforming actions into tasks and distributing them to worker nodes
B) The driver node stores the final result after computations are completed by worker nodes
C) The driver node only provides the user interface for monitoring the application
D) The driver node holds the DataFrame data and performs all computations locally


Solutions:

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

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