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Exam Code: Databricks-Certified-Associate-Developer-for-Apache-Spark-3.5 Vendor: Databricks Name: Databricks Certified Associate Developer for Apache Spark 3.5 – Python 136 Q&A with Explanation Last Update: Jul 14, 2026
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Databricks-Certified-Associate-Developer-for-Apache-Spark-3.5 FAQs

The exam validates proficiency in using PySpark DataFrame APIs for data manipulation and understanding Spark 3.5 architecture, focusing on tasks like filtering, joining, and aggregating data in a Databricks environment.

Use the official Spark 3.5 Python documentation, Databricks practice exams, and Dumpspedia courses with 80+ questions for hands-on practice. A digital notepad is available during the exam.

This certification validates your ability to build, optimize, and maintain Spark applications in production. It’s especially valued for data engineer, ETL developer, and big data engineer roles. Employers view it as proof you can handle distributed data processing at scale using Python, which is a critical skill in modern data pipelines.

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Databricks Certified Associate Developer for Apache Spark 3.5 – Python Questions and Answers

Questions 1

A data engineer observes that an upstream streaming source sends duplicate records, where duplicates share the same key and have at most a 30-minute difference in event_timestamp. The engineer adds:

dropDuplicatesWithinWatermark("event_timestamp", "30 minutes")

What is the result?

Options:

A.

It is not able to handle deduplication in this scenario

B.

It removes duplicates that arrive within the 30-minute window specified by the watermark

C.

It removes all duplicates regardless of when they arrive

D.

It accepts watermarks in seconds and the code results in an error

Questions 2

How can a Spark developer ensure optimal resource utilization when running Spark jobs in Local Mode for testing?

Options:

Options:

A.

Configure the application to run in cluster mode instead of local mode.

B.

Increase the number of local threads based on the number of CPU cores.

C.

Use the spark.dynamicAllocation.enabled property to scale resources dynamically.

D.

Set the spark.executor.memory property to a large value.

Questions 3

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

Options:

A.

df.write.json("path/to/file")

B.

df.write.mode("append").json("path/to/file")

C.

df.write.option("overwrite").json("path/to/file")

D.

df.write.mode("overwrite").json("path/to/file")