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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 Simulator
  • Exam Code: Associate-Developer-Apache-Spark-3.5
  • Exam Name: Databricks Certified Associate Developer for Apache Spark 3.5 - Python
  • Updated: Oct 10, 2026
  • Q & A: 135 Questions and Answers

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

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

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

Question #1

An engineer notices a significant increase in the job execution time during the execution of a Spark job. After some investigation, the engineer decides to check the logs produced by the Executors.
How should the engineer retrieve the Executor logs to diagnose performance issues in the Spark application?

  • A. Locate the executor logs on the Spark master node, typically under the /tmp directory.
  • B. Use the Spark UI to select the stage and view the executor logs directly from the stages tab.
  • C. Use the command spark-submit with the -verbose flag to print the logs to the console.
  • D. Fetch the logs by running a Spark job with the spark-sql CLI tool.
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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Question #2

A data engineer is working with a large JSON dataset containing order information. The dataset is stored in a distributed file system and needs to be loaded into a Spark DataFrame for analysis. The data engineer wants to ensure that the schema is correctly defined and that the data is read efficiently.
Which approach should the data scientist use to efficiently load the JSON data into a Spark DataFrame with a predefined schema?

  • A. Use spark.read.json() with the inferSchema option set to true
  • B. Define a StructType schema and use spark.read.schema(predefinedSchema).json() to load the data.
  • C. Use spark.read.format("json").load() and then use DataFrame.withColumn() to cast each column to the desired data type.
  • D. Use spark.read.json() to load the data, then use DataFrame.printSchema() to view the inferred schema, and finally use DataFrame.cast() to modify column types.
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Correct Answer: B  🗳️

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Question #3

7 of 55.
A developer has been asked to debug an issue with a Spark application. The developer identified that the data being loaded from a CSV file is being read incorrectly into a DataFrame.
The CSV file has been read using the following Spark SQL statement:
CREATE TABLE locations
USING csv
OPTIONS (path '/data/locations.csv')
The first lines of the command SELECT * FROM locations look like this:
| city | lat | long |
| ALTI Sydney | -33... | ... |
Which parameter can the developer add to the OPTIONS clause in the CREATE TABLE statement to read the CSV data correctly again?

  • A. 'header' 'false'
  • B. 'sep' ','
  • C. 'sep' '|'
  • D. 'header' 'true'
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Correct Answer: D  🗳️

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Question #4

An application architect has been investigating Spark Connect as a way to modernize existing Spark applications running in their organization.
Which requirement blocks the adoption of Spark Connect in this organization?

  • A. Complete Spark API support: the ability to migrate all existing code to Spark Connect without modification, including the RDD APIs
  • B. Debuggability: the ability to perform interactive debugging directly from the application code
  • C. Upgradability: the ability to upgrade the Spark applications independently from the Spark driver itself
  • D. Stability: isolation of application code and dependencies from each other and the Spark driver
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Correct Answer: A  🗳️

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Question #5

A data scientist is analyzing a large dataset and has written a PySpark script that includes several transformations and actions on a DataFrame. The script ends with a collect() action to retrieve the results.
How does Apache Spark™'s execution hierarchy process the operations when the data scientist runs this script?

  • A. The entire script is treated as a single job, which is then divided into multiple stages, and each stage is further divided into tasks based on data partitions.
  • B. Spark creates a single task for each transformation and action in the script, and these tasks are grouped into stages and jobs based on their dependencies.
  • C. The collect() action triggers a job, which is divided into stages at shuffle boundaries, and each stage is split into tasks that operate on individual data partitions.
  • D. The script is first divided into multiple applications, then each application is split into jobs, stages, and finally tasks.
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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