Databricks Certified Associate Developer for Apache Spark Exam Practice Questions (P. 1)
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Question #1
Which of the following describes the Spark driver?
- AThe Spark driver is responsible for performing all execution in all execution modes – it is the entire Spark application.
- BThe Spare driver is fault tolerant – if it fails, it will recover the entire Spark application.
- CThe Spark driver is the coarsest level of the Spark execution hierarchy – it is synonymous with the Spark application.
- DThe Spark driver is the program space in which the Spark application’s main method runs coordinating the Spark entire application.Most Voted
- EThe Spark driver is horizontally scaled to increase overall processing throughput of a Spark application.
Correct Answer:
D
D

The Spark driver is essentially the control center of a Spark application. It initiates and manages the applications main function, directing tasks across the cluster. Significantly, it engages with cluster managers to organize resources, oversees task execution, and gathers outputs from worker nodes. This key component is not inherently fault-tolerant nor does it scale horizontally, as its primary role is coordination and management of application execution, rather than performing computations or data storage itself.
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Question #2
Which of the following describes the relationship between nodes and executors?
- AExecutors and nodes are not related.
- BAnode is a processing engine running on an executor.
- CAn executor is a processing engine running on a node.Most Voted
- DThere are always the same number of executors and nodes.
- EThere are always more nodes than executors.
Correct Answer:
D
D

The relationship between nodes and execitors in Spark is often misunderstood. A node refers to any physical or virtual machine in a cluster, while an executor is a process running on such a node, specifically tasked with executing parts of the Spark application. Furthermore, it's not accurate to say there are always the same number of nodes and executors because configurations can vary widely, often with multiple executors per node to maximize resource use, contrary to what originally indicated by choosing answer D. From the analysis, answer C, which describes an executor as a processing engine running on a node, best represents the real-world setup in Spark environments.
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Question #3
Which of the following will occur if there are more slots than there are tasks?
- AThe Spark job will likely not run as efficiently as possible.Most Voted
- BThe Spark application will fail – there must be at least as many tasks as there are slots.
- CSome executors will shut down and allocate all slots on larger executors first.
- DMore tasks will be automatically generated to ensure all slots are being used.
- EThe Spark job will use just one single slot to perform all tasks.
Correct Answer:
D
D

In Apache Spark, having more slots than tasks does not result in creating more tasks automatically or shutting down executors to redistribute the slots. Instead, if there are more slots than tasks, the unused slots simply remain idle which can lead to an underutilization of resources. However, the Spark application continues to perform the available tasks with the slots that have tasks assigned. It's essential to adjust the task and slot configuration based on demand to avoid resource wastage and improve efficiency, rather than expecting dynamic slot reallocation or automatic task generation.
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Question #4
Which of the following is the most granular level of the Spark execution hierarchy?
- ATaskMost Voted
- BExecutor
- CNode
- DJob
- ESlot
Correct Answer:
A
A

In the Spark execution hierarchy, a task is indeed the smallest unit. Each task is essentially a unit of work that focuses on a segmented portion of data. This division allows tasks to be handled independently and in parallel across multiple executors, leading to efficient data processing. Tasks are crucial as they execute precise operations such as transformations or actions on specific data subsets, managed by the Spark driver.
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Question #5
Which of the following statements about Spark jobs is incorrect?
- AJobs are broken down into stages.
- BThere are multiple tasks within a single job when a DataFrame has more than one partition.
- CJobs are collections of tasks that are divided up based on when an action is called.
- DThere is no way to monitor the progress of a job.Most Voted
- EJobs are collections of tasks that are divided based on when language variables are defined.
Correct Answer:
D
D

Indeed, there's a mix-up in the question with two statements that are notably incorrect. The correct answer marked as D, which states "There is no way to monitor the progress of a job," is indeed not true. Spark offers various tools such as the Spark UI and Spark History Server to effectively monitor job progress, providing details on tasks, stages, and resource usage, which contradicts the claim in the statement. Additionally, statement E is also incorrect because task divisions within a job are determined by when actions are called, not due to the definition of language variables.
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