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Download Databricks Certified Associate Developer for Apache Spark 3.0 Exam Exam Dumps

NEW QUESTION 51
Which of the following describes the role of tasks in the Spark execution hierarchy?

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

Answer: D

Explanation:
Explanation
Stages with narrow dependencies can be grouped into one task.
Wrong, tasks with narrow dependencies can be grouped into one stage.
Tasks with wide dependencies can be grouped into one stage.
Wrong, since a wide transformation causes a shuffle which always marks the boundary of a stage. So, you cannot bundle multiple tasks that have wide dependencies into a stage.
Tasks are the second-smallest element in the execution hierarchy.
No, they are the smallest element in the execution hierarchy.
Within one task, the slots are the unit of work done for each partition of the data.
No, tasks are the unit of work done per partition. Slots help Spark parallelize work. An executor can have multiple slots which enable it to process multiple tasks in parallel.

 

NEW QUESTION 52
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?

A. itemsDf.cache(StorageLevel.MEMORY_AND_DISK)B. itemsDf.cache()C. itemsDf.write.option('destination', 'memory').save()D. itemsDf.persist(StorageLevel.MEMORY_ONLY)E. itemsDf.store()

Answer: B

Explanation:
Explanation
The key to solving this question is knowing (or reading in the documentation) that, by default, cache() stores values to memory and writes any partitions for which there is insufficient memory to disk. persist() can achieve the exact same behavior, however not with the StorageLevel.MEMORY_ONLY option listed here. It is also worth noting that cache() does not have any arguments.
If you have troubles finding the storage level information in the documentation, please also see this student Q&A thread that sheds some light here.
Static notebook | Dynamic notebook: See test 2

 

NEW QUESTION 53
Which of the following code blocks reads JSON file imports.json into a DataFrame?

A. spark.read.json("/FileStore/imports.json")B. spark.read.format("json").path("/FileStore/imports.json")C. spark.read().json("/FileStore/imports.json")D. spark.read().mode("json").path("/FileStore/imports.json")E. spark.read("json", "/FileStore/imports.json")

Answer: A

Explanation:
Explanation
Static notebook | Dynamic notebook: See test 1
(https://flrs.github.io/spark_practice_tests_code/#1/25.html ,
https://bit.ly/sparkpracticeexams_import_instructions)

 

NEW QUESTION 54
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