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Databricks Exam Databricks Certified Associate Developer for Apache Spark 3.0 Topic 2 Question 58 Discussion

Actual exam question for Databricks's Databricks Certified Associate Developer for Apache Spark 3.0 exam
Question #: 58
Topic #: 2
[All Databricks Certified Associate Developer for Apache Spark 3.0 Questions]

Which of the following is the deepest level in Spark's execution hierarchy?

Show Suggested Answer Hide Answer
Suggested Answer: B

Correct code block:

transactionsDf.select('storeId').printSchema()

The difficulty of this Question: is that it is hard to solve with the stepwise first-to-last-gap approach that has worked well for similar questions, since the answer options are so different from

one

another. Instead, you might want to eliminate answers by looking for patterns of frequently wrong answers.

A first pattern that you may recognize by now is that column names are not expressed in quotes. For this reason, the answer that includes storeId should be eliminated.

By now, you may have understood that the DataFrame.limit() is useful for returning a specified amount of rows. It has nothing to do with specific columns. For this reason, the answer that resolves to

limit('storeId') can be eliminated.

Given that we are interested in information about the data type, you should Question: whether the answer that resolves to limit(1).columns provides you with this information. While

DataFrame.columns is a valid call, it will only report back column names, but not column types. So, you can eliminate this option.

The two remaining options either use the printSchema() or print_schema() command. You may remember that DataFrame.printSchema() is the only valid command of the two. The select('storeId')

part just returns the storeId column of transactionsDf - this works here, since we are only interested in that column's type anyways.

More info: pyspark.sql.DataFrame.printSchema --- PySpark 3.1.2 documentation

Static notebook | Dynamic notebook: See test 3, Question: 57 (Databricks import instructions)


Contribute your Thoughts:

Ressie
29 days ago
I'm picturing a stage on a theater where the actors (tasks) perform. Yeah, Stage has to be the deepest level, right?
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Margarett
1 months ago
I'm going to go with Stage. It just makes the most sense to me in terms of Spark's execution hierarchy.
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Felice
1 months ago
Hold on, did you say Slot? I thought that was just a fancy name for a CPU core. Spark doesn't really care about that level of detail, does it?
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Flo
2 days ago
D) Slot
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Carmelina
3 days ago
C) Executor
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Rosio
15 days ago
B) Task
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Rolland
19 days ago
A) Job
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Jesusa
1 months ago
Hmm, I'm torn between Executor and Slot. Executor seems like the most logical choice, but Slot also sounds like it could be the deepest level.
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Alba
2 months ago
I'm pretty sure the correct answer is Stage. That's the collection of tasks that Spark needs to execute to complete a Job.
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Dean
10 days ago
I agree with you, Stage is the correct answer. It represents a set of parallel tasks that all perform the same computation.
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Azzie
17 days ago
I believe it's Task. That's the smallest unit of work that Spark schedules.
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Luz
19 days ago
I think the correct answer is actually Executor. It's responsible for actually running the tasks on the worker nodes.
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Truman
24 days ago
So, the correct answer is Task, not Stage. It's important to understand the different levels in Spark's execution hierarchy.
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Shaquana
1 months ago
Actually, the deepest level in Spark's execution hierarchy is Task. It's the smallest unit of work that Spark schedules.
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Ines
1 months ago
I think the correct answer is Stage. It's the collection of tasks that Spark needs to execute to complete a Job.
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Marion
2 months ago
I'm not sure, but I think it might be A) Job because it initiates the execution process.
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Malcom
2 months ago
I agree with Ciara, because a Stage is made up of multiple Tasks.
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Lilli
2 months ago
I think the deepest level in Spark's execution hierarchy is the Task. It's the smallest unit of work that Spark can assign to an Executor.
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Gertude
1 months ago
So, the Task is just a small part of what the Executor does.
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Maxima
2 months ago
Executor makes sense, it's where the actual computation happens.
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Burma
2 months ago
I believe it's actually the Executor, which is responsible for executing tasks on a given node.
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Paulina
2 months ago
I think the deepest level in Spark's execution hierarchy is the Task.
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Ciara
2 months ago
I think the answer is E) Stage.
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