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: Aug 08, 2026
  • Q & A: 135 Questions and Answers

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

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

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

1. 46 of 55.
A data engineer is implementing a streaming pipeline with watermarking to handle late-arriving records.
The engineer has written the following code:
inputStream \
.withWatermark("event_time", "10 minutes") \
.groupBy(window("event_time", "15 minutes"))
What happens to data that arrives after the watermark threshold?

A) Records that arrive later than the watermark threshold (10 minutes) will automatically be included in the aggregation if they fall within the 15-minute window.
B) The watermark ensures that late data arriving within 10 minutes of the latest event time will be processed and included in the windowed aggregation.
C) Data arriving more than 10 minutes after the latest watermark will still be included in the aggregation but will be placed into the next window.
D) Any data arriving more than 10 minutes after the watermark threshold will be ignored and not included in the aggregation.


2. 31 of 55.
Given a DataFrame df that has 10 partitions, after running the code:
df.repartition(20)
How many partitions will the result DataFrame have?

A) 20
B) Same number as the cluster executors
C) 10
D) 5


3. A data engineer uses a broadcast variable to share a DataFrame containing millions of rows across executors for lookup purposes. What will be the outcome?

A) The job may fail because the driver does not have enough CPU cores to serialize the large DataFrame
B) The job may fail if the memory on each executor is not large enough to accommodate the DataFrame being broadcasted
C) The job will hang indefinitely as Spark will struggle to distribute and serialize such a large broadcast variable to all executors
D) The job may fail if the executors do not have enough CPU cores to process the broadcasted dataset


4. 29 of 55.
A Spark application is experiencing performance issues in client mode due to the driver being resource-constrained.
How should this issue be resolved?

A) Switch the deployment mode to local mode.
B) Add more executor instances to the cluster.
C) Switch the deployment mode to cluster mode.
D) Increase the driver memory on the client machine.


5. What is a feature of Spark Connect?

A) It has built-in authentication
B) It supports DataStreamReader, DataStreamWriter, StreamingQuery, and Streaming APIs
C) Supports DataFrame, Functions, Column, SparkContext PySpark APIs
D) It supports only PySpark applications


Solutions:

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

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