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Databricks (Databricks Certified Associate Developer for Apache Spark 3.0) Exam Questions PDF

Databricks Certified Associate Developer for Apache Spark 3.0 pdf

Exam Code: Databricks Certified Associate Developer for Apache Spark 3.0
Exam Name: Databricks Certified Associate Developer for Apache Spark 3.0
Updated: Jun 07, 2026
180 Questions and Answers
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Exam Code: Databricks Certified Associate Developer for Apache Spark 3.0
Exam Name: Databricks Certified Associate Developer for Apache Spark 3.0
Updated: Jun 07, 2026
180 Questions and Answers
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Exam Code: Databricks Certified Associate Developer for Apache Spark 3.0
Exam Name: Databricks Certified Associate Developer for Apache Spark 3.0
Updated: Jun 07, 2026
180 Questions and Answers
Practice Test: ~~$59.00~~ $29.5
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Databricks Databricks Certified Associate Developer for Apache Spark 3.0 Exam Questions

Databricks Certified Associate Developer for Apache Spark 3.0 exams.

Question — (Single Choice)

Which of the following options describes the responsibility of the executors in Spark?

Choose the answer:

A The executors accept jobs from the driver, analyze those jobs, and return results to the driver.
B The executors accept tasks from the driver, execute those tasks, and return results to the cluster manager.
C The executors accept tasks from the driver, execute those tasks, and return results to the driver.
D The executors accept tasks from the cluster manager, execute those tasks, and return results to the driver.
E The executors accept jobs from the driver, plan those jobs, and return results to the cluster manager.

Question — (Single Choice)

Which of the following describes the role of tasks in the Spark execution hierarchy?

Choose the answer:

A Tasks are the smallest element in the execution hierarchy.
B Within one task, the slots are the unit of work done for each partition of the data.
C Tasks are the second - smallest element in the execution hierarchy.
D Stages with narrow dependencies can be grouped into one task.
E Tasks with wide dependencies can be grouped into one stage.

Question — (Single Choice)

Which of the following describes the role of the cluster manager?

Choose the answer:

A The cluster manager schedules tasks on the cluster in client mode.
B The cluster manager schedules tasks on the cluster in local mode.
C The cluster manager allocates resources to Spark applications and maintains the executor processes in client mode.
D The cluster manager allocates resources to Spark applications and maintains the executor processes in remote mode.
E The cluster manager allocates resources to the DataFrame manager.

Question — (Single Choice)

Which of the following is the idea behind dynamic partition pruning in Spark?

Choose the answer:

A Dynamic partition pruning is intended to skip over the data you do not need in the results of a query.
B Dynamic partition pruning concatenates columns of similar data types to optimize join performance.
C Dynamic partition pruning performs wide transformations on disk instead of in memory.
D Dynamic partition pruning reoptimizes physical plans based on data types and broadcast variables.
E Dynamic partition pruning reoptimizes query plans based on runtime statistics collected during query execution.

Question — (Single Choice)

Which of the following is one of the big performance advantages that Spark has over Hadoop?

Choose the answer:

A Spark achieves great performance by storing data in the DAG format, whereas Hadoop can only use parquet files.
B Spark achieves higher resiliency for queries since, different from Hadoop, it can be deployed on Kubernetes.
C Spark achieves great performance by storing data and performing computation in memory, whereas large jobs in Hadoop require a large amount of relatively slow disk I/O operations.
D Spark achieves great performance by storing data in the HDFS format, whereas Hadoop can only use parquet files.
E Spark achieves performance gains for developers by extending Hadoop's DataFrames with a user - friendly API.

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Final Thoughts

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