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Databricks Certified Associate Developer for Apache Spark 3.5 - Python : 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: Sep 09, 2026
- Q&As: 135 Questions and Answers
About Databricks Associate-Developer-Apache-Spark-3.5 Real Exam
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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Using Spark SQL | 20% | - Spark SQL Operations
|
| Structured Streaming | 10% | - Streaming Applications
|
| Apache Spark Architecture and Components | 20% | - Spark Architecture
|
| Developing Apache Spark DataFrame API Applications | 30% | - DataFrame Operations
|
| Troubleshooting and Tuning | 10% | - Performance Optimization
|
| Using Pandas API on Spark | 5% | - Pandas API
|
| Using Spark Connect to Deploy Applications | 5% | - Spark Connect
|
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. 1
- C. 10
- D. Same number as the cluster executors
Answer: C
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Question #2
A developer wants to test Spark Connect with an existing Spark application.
What are the two alternative ways the developer can start a local Spark Connect server without changing their existing application code? (Choose 2 answers)
- A. Set the environment variable SPARK_REMOTE="sc://localhost" before starting the pyspark shell
- B. Ensure the Spark property spark.connect.grpc.binding.port is set to 15002 in the application code
- C. Add .remote("sc://localhost") to their SparkSession.builder calls in their Spark code
- D. Execute their pyspark shell with the option --remote "sc://localhost"
- E. Execute their pyspark shell with the option --remote "https://localhost"
Answer: A,D
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Question #3
A developer initializes a SparkSession:
spark = SparkSession.builder \
.appName("Analytics Application") \
.getOrCreate()
Which statement describes the spark SparkSession?
- A. The getOrCreate() method explicitly destroys any existing SparkSession and creates a new one.
- B. If a SparkSession already exists, this code will return the existing session instead of creating a new one.
- C. A new SparkSession is created every time the getOrCreate() method is invoked.
- D. A SparkSession is unique for each appName, and calling getOrCreate() with the same name will return an existing SparkSession once it has been created.
Answer: B
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Question #4
A data scientist of an e-commerce company is working with user data obtained from its subscriber database and has stored the data in a DataFrame df_user. Before further processing the data, the data scientist wants to create another DataFrame df_user_non_pii and store only the non-PII columns in this DataFrame. The PII columns in df_user are first_name, last_name, email, and birthdate.
Which code snippet can be used to meet this requirement?
- A. df_user_non_pii = df_user.drop("first_name", "last_name", "email", "birthdate")
- B. df_user_non_pii = df_user.drop("first_name", "last_name", "email", "birthdate")
- C. df_user_non_pii = df_user.dropfields("first_name, last_name, email, birthdate")
- D. df_user_non_pii = df_user.dropfields("first_name", "last_name", "email", "birthdate")
Answer: A
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Question #5
A Spark application suffers from too many small tasks due to excessive partitioning. How can this be fixed without a full shuffle?
Options:
- A. Use the repartition() transformation with a lower number of partitions
- B. Use the distinct() transformation to combine similar partitions
- C. Use the coalesce() transformation with a lower number of partitions
- D. Use the sortBy() transformation to reorganize the data
Answer: C
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