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

Databricks Associate-Developer-Apache-Spark-3.5 exam
  • Exam Code: Associate-Developer-Apache-Spark-3.5
  • Exam Name: Databricks Certified Associate Developer for Apache Spark 3.5 - Python
  • Updated: Sep 26, 2026
  • Q & A: 135 Questions and Answers
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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:

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

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

Question #1

28 of 55.
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. select
  • B. coalesce
  • C. filter
  • D. groupBy
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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Question #2

38 of 55.
A data engineer is working with Spark SQL and has a large JSON file stored at /data/input.json.
The file contains records with varying schemas, and the engineer wants to create an external table in Spark SQL that:
Reads directly from /data/input.json.
Infers the schema automatically.
Merges differing schemas.
Which code snippet should the engineer use?

  • A. CREATE EXTERNAL TABLE users
    USING json
    OPTIONS (path '/data/input.json', mergeAll 'true');
  • B. CREATE TABLE users
    USING json
    OPTIONS (path '/data/input.json');
  • C. CREATE EXTERNAL TABLE users
    USING json
    OPTIONS (path '/data/input.json', inferSchema 'true');
  • D. CREATE EXTERNAL TABLE users
    USING json
    OPTIONS (path '/data/input.json', mergeSchema 'true');
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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Question #3

A Data Analyst is working on the DataFrame sensor_df, which contains two columns:
Which code fragment returns a DataFrame that splits the record column into separate columns and has one array item per row?
A)

B)

C)

D)

  • A. exploded_df = sensor_df.withColumn("record_exploded", explode("record")) exploded_df = exploded_df.select("record_datetime", "sensor_id", "status", "health")
  • B. exploded_df = exploded_df.select(
    "record_datetime",
    "record_exploded.sensor_id",
    "record_exploded.status",
    "record_exploded.health"
    )
    exploded_df = sensor_df.withColumn("record_exploded", explode("record"))
  • C. exploded_df = exploded_df.select("record_datetime", "record_exploded")
  • D. exploded_df = exploded_df.select(
    "record_datetime",
    "record_exploded.sensor_id",
    "record_exploded.status",
    "record_exploded.health"
    )
    exploded_df = sensor_df.withColumn("record_exploded", explode("record"))
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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Question #4

An engineer wants to join two DataFrames df1 and df2 on the respective employee_id and emp_id columns:
df1: employee_id INT, name STRING
df2: emp_id INT, department STRING
The engineer uses:
result = df1.join(df2, df1.employee_id == df2.emp_id, how='inner')
What is the behaviour of the code snippet?

  • A. The code works as expected because the join condition explicitly matches employee_id from df1 with emp_id from df2
  • B. The code fails to execute because the column names employee_id and emp_id do not match automatically
  • C. The code fails to execute because PySpark does not support joining DataFrames with a different structure
  • D. The code fails to execute because it must use on='employee_id' to specify the join column explicitly
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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Question #5

14 of 55.
A developer created a DataFrame with columns color, fruit, and taste, and wrote the data to a Parquet directory using:
df.write.partitionBy("color", "taste").parquet("/path/to/output")
What is the result of this code?

  • A. It stores all data in a single Parquet file.
  • B. It appends new partitions to an existing Parquet file.
  • C. It throws an error if there are null values in either partition column.
  • D. It creates separate directories for each unique combination of color and taste.
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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