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

"Databricks Certified Associate Developer for Apache Spark 3.5 - Python", also known as Associate-Developer-Apache-Spark-3.5 exam, is a Databricks Certification. With the complete collection of questions and answers, BraindumpsPass has assembled to take you through 135 Q&As to your Associate-Developer-Apache-Spark-3.5 Exam preparation. In the Associate-Developer-Apache-Spark-3.5 exam resources, you will cover every field and category in Databricks Certification Certification helping to ready you for your successful Databricks Certification.

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  • Updated on: Aug 02, 2026
  • No. of Questions: 135 Questions & Answers

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

SectionWeightObjectives
Topic 1: Apache Spark Architecture and Components20%- Spark architecture overview
- Fault tolerance and garbage collection
- Execution hierarchy and lazy evaluation
- Shuffling, actions, and broadcasting
- Execution and deployment modes
Topic 2: Using Spark SQL20%- Using catalog and metadata APIs
- Running SQL queries
- Working with functions and expressions
- Integrating Spark SQL with DataFrames
Topic 3: Troubleshooting and Tuning Apache Spark DataFrame API Applications10%- Managing memory and resource usage
- Debugging and logging
- Identifying performance bottlenecks
- Optimizing transformations and actions
Topic 4: Using Pandas API on Apache Spark5%- Converting between Pandas and Spark structures
- Key differences and limitations
- Overview of Pandas API on Spark
Topic 5: Developing Apache Spark DataFrame API Applications30%- Partitioning and bucketing data
- Reading and writing data in various formats
- Handling missing values and data quality
- Joining and combining datasets
- Creating DataFrames and defining schemas
- User-defined functions (UDFs)
- Filtering, sorting, and aggregating data
- Selecting, renaming, and modifying columns
Topic 6: Using Spark Connect to Deploy Applications5%- Connecting to remote Spark clusters
- Running applications via Spark Connect
- Spark Connect architecture
Topic 7: Structured Streaming10%- Output modes and triggers
- Fault tolerance and state management
- Defining streaming queries
- Streaming concepts and architecture

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

1. 9 of 55.
Given the code fragment:
import pyspark.pandas as ps
pdf = ps.DataFrame(data)
Which method is used to convert a Pandas API on Spark DataFrame (pyspark.pandas.DataFrame) into a standard PySpark DataFrame (pyspark.sql.DataFrame)?

A) pdf.to_spark()
B) pdf.to_pandas()
C) pdf.to_dataframe()
D) pdf.spark()


2. 15 of 55.
A data engineer is working on a Streaming DataFrame (streaming_df) with the following streaming data:
id
name
count
timestamp
1
Delhi
20
2024-09-19T10:11
1
Delhi
50
2024-09-19T10:12
2
London
50
2024-09-19T10:15
3
Paris
30
2024-09-19T10:18
3
Paris
20
2024-09-19T10:20
4
Washington
10
2024-09-19T10:22
Which operation is supported with streaming_df?

A) streaming_df.filter("count < 30")
B) streaming_df.count()
C) streaming_df.select(countDistinct("name"))
D) streaming_df.show()


3. 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.dropfields("first_name", "last_name", "email", "birthdate")
C) df_user_non_pii = df_user.drop("first_name", "last_name", "email", "birthdate")
D) df_user_non_pii = df_user.dropfields("first_name, last_name, email, birthdate")


4. 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) groupBy
C) coalesce
D) filter


5. 11 of 55.
Which Spark configuration controls the number of tasks that can run in parallel on an executor?

A) spark.executor.cores
B) spark.task.maxFailures
C) spark.sql.shuffle.partitions
D) spark.executor.memory


Solutions:

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

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