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NEW QUESTION 52
Which of the following describes characteristics of the Spark driver?
Answer: C
Explanation:
Explanation
The Spark driver requests the transformation of operations into DAG computations from the worker nodes.
No, the Spark driver transforms operations into DAG computations itself.
If set in the Spark configuration, Spark scales the Spark driver horizontally to improve parallel processing performance.
No. There is always a single driver per application, but one or more executors.
The Spark driver processes partitions in an optimized, distributed fashion.
No, this is what executors do.
In a non-interactive Spark application, the Spark driver automatically creates the SparkSession object.
Wrong. In a non-interactive Spark application, you need to create the SparkSession object. In an interactive Spark shell, the Spark driver instantiates the object for you.
NEW QUESTION 53
Which of the following code blocks silently writes DataFrame itemsDf in avro format to location fileLocation if a file does not yet exist at that location?
Answer: C
Explanation:
Explanation
The trick in this question is knowing the "modes" of the DataFrameWriter. Mode ignore will ignore if a file already exists and not replace that file, but also not throw an error. Mode errorifexists will throw an error, and is the default mode of the DataFrameWriter. The question NO:
explicitly calls for the DataFrame to be "silently" written if it does not exist, so you need to specify mode("ignore") here to avoid having Spark communicate any error to you if the file already exists.
The `overwrite' mode would not be right here, since, although it would be silent, it would overwrite the already-existing file. This is not what the question asks for.
It is worth noting that the option starting with spark.DataFrameWriter(itemsDf) cannot work, since spark references the SparkSession object, but that object does not provide the DataFrameWriter.
As you can see in the documentation (below), DataFrameWriter is part of PySpark's SQL API, but not of its SparkSession API.
More info:
DataFrameWriter: pyspark.sql.DataFrameWriter.save - PySpark 3.1.1 documentation SparkSession API: Spark SQL - PySpark 3.1.1 documentation Static notebook | Dynamic notebook: See test 1
NEW QUESTION 54
Which of the following statements about Spark's execution hierarchy is correct?
Answer: E
Explanation:
Explanation
In Spark's execution hierarchy, a job may reach over multiple stage boundaries.
Correct. A job is a sequence of stages, and thus may reach over multiple stage boundaries.
In Spark's execution hierarchy, tasks are one layer above slots.
Incorrect. Slots are not a part of the execution hierarchy. Tasks are the lowest layer.
In Spark's execution hierarchy, a stage comprises multiple jobs.
No. It is the other way around - a job consists of one or multiple stages.
In Spark's execution hierarchy, executors are the smallest unit.
False. Executors are not a part of the execution hierarchy. Tasks are the smallest unit!
In Spark's execution hierarchy, manifests are one layer above jobs.
Wrong. Manifests are not a part of the Spark ecosystem.
NEW QUESTION 55
Which of the following describes slots?
Slots are the communication interface for executors and are used for receiving commands and sending results to the driver.D. A Java Virtual Machine (JVM) working as an executor can be considered as a pool of slots for task execution.
Answer: D
Explanation:
Explanation
Slots are the communication interface for executors and are used for receiving commands and sending results to the driver.
Wrong, executors communicate with the driver directly.
Slots are dynamically created and destroyed in accordance with an executor's workload.
No, Spark does not actively create and destroy slots in accordance with the workload. Per executor, slots are made available in accordance with how many cores per executor (property spark.executor.cores) and how many CPUs per task (property spark.task.cpus) the Spark configuration calls for.
A slot is always limited to a single core.
No, a slot can span multiple cores. If a task would require multiple cores, it would have to be executed through a slot that spans multiple cores.
In Spark documentation, "core" is often used interchangeably with "thread", although "thread" is the more accurate word. A single physical core may be able to make multiple threads available. So, it is better to say that a slot can span multiple threads.
To optimize I/O performance, Spark stores data on disk in multiple slots.
No - Spark stores data on disk in multiple partitions, not slots.
More info: Spark Architecture | Distributed Systems Architecture
NEW QUESTION 56
Which of the following code blocks reads JSON file imports.json into a DataFrame?
Answer: E
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 57
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