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: Oct 07, 2026     Q & A: 135 Questions and Answers

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

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

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

Question #1

A data engineer writes the following code to join two DataFrames df1 and df2:
df1 = spark.read.csv("sales_data.csv") # ~10 GB
df2 = spark.read.csv("product_data.csv") # ~8 MB
result = df1.join(df2, df1.product_id == df2.product_id)

Which join strategy will Spark use?

  • A. Broadcast join, as df2 is smaller than the default broadcast threshold
  • B. Shuffle join, because AQE is not enabled, and Spark uses a static query plan
  • C. Shuffle join because no broadcast hints were provided
  • D. Shuffle join, as the size difference between df1 and df2 is too large for a broadcast join to work efficiently
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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Question #2

Which configuration can be enabled to optimize the conversion between Pandas and PySpark DataFrames using Apache Arrow?

  • A. spark.conf.set("spark.sql.execution.arrow.enabled", "true")
  • B. spark.conf.set("spark.sql.execution.arrow.pyspark.enabled", "true")
  • C. spark.conf.set("spark.pandas.arrow.enabled", "true")
  • D. spark.conf.set("spark.sql.arrow.pandas.enabled", "true")
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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Question #3

A data engineer wants to create a Streaming DataFrame that reads from a Kafka topic called feed.

Which code fragment should be inserted in line 5 to meet the requirement?
Code context:
spark \
.readStream \
.format("kafka") \
.option("kafka.bootstrap.servers", "host1:port1,host2:port2") \
.[LINE 5] \
.load()
Options:

  • A. .option("topic", "feed")
  • B. .option("subscribe", "feed")
  • C. .option("subscribe.topic", "feed")
  • D. .option("kafka.topic", "feed")
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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Question #4

26 of 55.
A data scientist at 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 scientist wants to create another DataFrame df_user_non_pii and store only the non-PII columns.
The PII columns in df_user are name, email, and birthdate.
Which code snippet can be used to meet this requirement?

  • A. df_user_non_pii = df_user.select("name", "email", "birthdate")
  • B. df_user_non_pii = df_user.drop("name", "email", "birthdate")
  • C. df_user_non_pii = df_user.dropFields("name", "email", "birthdate")
  • D. df_user_non_pii = df_user.remove("name", "email", "birthdate")
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Explanation: Only visible for PracticeTorrent members. You can sign-up / login (it's free).

Question #5

2 of 55. Which command overwrites an existing JSON file when writing a DataFrame?

  • A. df.write.option("overwrite").json("path/to/file")
  • B. df.write.mode("append").json("path/to/file")
  • C. df.write.mode("overwrite").json("path/to/file")
  • D. df.write.json("path/to/file")
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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