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Exam Code: Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 Vendor: Databricks Name: Databricks Certified Associate Developer for Apache Spark 3.0 Exam 180 Q&A with Explanation Last Update: Jul 14, 2026
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Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 FAQs

What is the Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 Exam?

The Databricks Certified Associate Developer for Apache Spark 3.0 exam is a certification test that validates a candidate's ability to develop Spark applications using the Apache Spark 3.0 platform.

What types of job roles can I pursue with a Databricks Certified Associate Developer for Apache Spark 3.0 certification?

With this certification, you can pursue roles such as Data Engineer, Spark Developer, Big Data Engineer, and Data Scientist.

How does the Databricks certification impact salary?

Professionals with a Databricks certification typically see a salary increase, with average salaries ranging from $90,000 to $130,000 annually, depending on experience and location.

How does the Databricks certification benefit my organization?

The certification benefits organizations by ensuring that employees have the necessary skills to develop efficient and scalable Spark applications, leading to better data processing and analytics capabilities.

What impact does the Databricks certification have on career flexibility?

The certification enhances career flexibility by providing a strong foundation in Spark development, making you eligible for various roles across different industries that rely on big data and analytics.

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Databricks Certified Associate Developer for Apache Spark 3.0 Exam Questions and Answers

Questions 1

Which of the following code blocks reads in parquet file /FileStore/imports.parquet as a DataFrame?

Options:

A.

spark.mode("parquet").read("/FileStore/imports.parquet")

B.

spark.read.path("/FileStore/imports.parquet", source="parquet")

C.

spark.read().parquet("/FileStore/imports.parquet")

D.

spark.read.parquet("/FileStore/imports.parquet")

E.

spark.read().format('parquet').open("/FileStore/imports.parquet")

Questions 2

Which of the following describes a way for resizing a DataFrame from 16 to 8 partitions in the most efficient way?

Options:

A.

Use operation DataFrame.repartition(8) to shuffle the DataFrame and reduce the number of partitions.

B.

Use operation DataFrame.coalesce(8) to fully shuffle the DataFrame and reduce the number of partitions.

C.

Use a narrow transformation to reduce the number of partitions.

D.

Use a wide transformation to reduce the number of partitions.

Use operation DataFrame.coalesce(0.5) to halve the number of partitions in the DataFrame.

Questions 3

Which of the following code blocks stores DataFrame itemsDf in executor memory and, if insufficient memory is available, serializes it and saves it to disk?

Options:

A.

itemsDf.persist(StorageLevel.MEMORY_ONLY)

B.

itemsDf.cache(StorageLevel.MEMORY_AND_DISK)

C.

itemsDf.store()

D.

itemsDf.cache()

E.

itemsDf.write.option('destination', 'memory').save()

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