Certified Associate Developer for Apache Spark

Here you have the best Databricks Certified Associate Developer for Apache Spark practice exam questions

You have 188 total questions to study from
Each page has 5 questions, making a total of 38 pages
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This questions were last updated on October 26, 2025
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Question 1 of 188

Which of the following describes the Spark driver?

The Spark driver is responsible for performing all execution in all execution modes – it is the entire Spark application.

The Spare driver is fault tolerant – if it fails, it will recover the entire Spark application.

The Spark driver is the coarsest level of the Spark execution hierarchy – it is synonymous with the Spark application.

The Spark driver is the program space in which the Spark application’s main method runs coordinating the Spark entire application.

The Spark driver is horizontally scaled to increase overall processing throughput of a Spark application.

Correct Answer: D

The Spark driver is the program space where the Spark application's main method runs, coordinating the entire Spark application. It is responsible for managing the application's execution plan, resource allocation, task distribution, and monitoring the application's state throughout its execution. The driver orchestrates the execution process by communicating with the cluster manager to acquire necessary resources and distributing tasks to worker nodes appropriately.

Question 2 of 188

Which of the following describes the relationship between nodes and executors?

Executors and nodes are not related.

Anode is a processing engine running on an executor.

An executor is a processing engine running on a node.

There are always the same number of executors and nodes.

There are always more nodes than executors.

Correct Answer: C

An executor is a processing engine running on a node. Executors are worker processes that run on the nodes of a cluster and are responsible for executing tasks assigned by the driver program. They handle data processing and execute the operations required for a Spark application. Hence, the correct relationship is that executors run on nodes.

Question 3 of 188

Which of the following will occur if there are more slots than there are tasks?

The Spark job will likely not run as efficiently as possible.

The Spark application will fail – there must be at least as many tasks as there are slots.

Some executors will shut down and allocate all slots on larger executors first.

More tasks will be automatically generated to ensure all slots are being used.

The Spark job will use just one single slot to perform all tasks.

Correct Answer: A

If there are more slots than tasks in a Spark job, some slots will remain idle. This will result in inefficient utilization of resources as these idle slots represent unused processing capacity. Thus, the Spark job will not run as efficiently as possible but will still complete successfully.

Question 4 of 188

Which of the following is the most granular level of the Spark execution hierarchy?

Task

Executor

Node

Job

Slot

Correct Answer: A

In the Spark execution hierarchy, the most granular level is the task. A task represents a unit of work that is executed on a partitioned portion of the data in parallel. Tasks are created by the Spark driver program and assigned to individual executors for execution. Each task operates on a subset of the data and performs a specific operation defined by the Spark application, such as a transformation or an action.

Question 5 of 188

Which of the following statements about Spark jobs is incorrect?

Jobs are broken down into stages.

There are multiple tasks within a single job when a DataFrame has more than one partition.

Jobs are collections of tasks that are divided up based on when an action is called.

There is no way to monitor the progress of a job.

Jobs are collections of tasks that are divided based on when language variables are defined.

Correct Answer: E

In Spark, jobs are collections of tasks that are divided based on when an action is called, not based on when language variables are defined. This statement is incorrect because it misrepresents how tasks are grouped within a job. Spark creates a job whenever an action (like count() or collect()) is called on a DataFrame or RDD. These jobs are then broken down into stages and tasks, orchestrated based on the logical execution plan derived from the actions, not from the definition of language variables.