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NEW QUESTION 53
Which of the following describes properties of a shuffle?

• A. A shuffle is one of many actions in Spark.
• B. In a shuffle, Spark writes data to disk.
• C. Operations involving shuffles are never evaluated lazily.
• D. Shuffles involve only single partitions.
• E. Shuffles belong to a class known as "full transformations".

Answer: B

Explanation:
Explanation
In a shuffle, Spark writes data to disk.
Correct! Spark's architecture dictates that intermediate results during a shuffle are written to disk.
A shuffle is one of many actions in Spark.
Incorrect. A shuffle is a transformation, but not an action.
Shuffles involve only single partitions.
No, shuffles involve multiple partitions. During a shuffle, Spark generates output partitions from multiple input partitions.
Operations involving shuffles are never evaluated lazily.
Wrong. A shuffle is a costly operation and Spark will evaluate it as lazily as other transformations. This is, until a subsequent action triggers its evaluation.
Shuffles belong to a class known as "full transformations".
Not quite. Shuffles belong to a class known as "wide transformations". "Full transformation" is not a relevant term in Spark.
More info: Spark - The Definitive Guide, Chapter 2 and Spark: disk I/O on stage boundaries explanation - Stack Overflow

NEW QUESTION 54
Which of the following code blocks stores a part of the data in DataFrame itemsDf on executors?

• A. itemsDf.cache().count()
• B. itemsDf.cache().filter()
• C. cache(itemsDf)
• D. itemsDf.rdd.storeCopy()
• E. itemsDf.cache(eager=True)

Answer: A

Explanation:
Explanation
Caching means storing a copy of a partition on an executor, so it can be accessed quicker by subsequent operations, instead of having to be recalculated. cache() is a lazily-evaluated method of the DataFrame. Since count() is an action (while filter() is not), it triggers the caching process.
More info: pyspark.sql.DataFrame.cache - PySpark 3.1.2 documentation, Learning Spark, 2nd Edition, Chapter 7 Static notebook | Dynamic notebook: See test 2

NEW QUESTION 55
The code block displayed below contains one or more errors. The code block should load parquet files at location filePath into a DataFrame, only loading those files that have been modified before
2029-03-20 05:44:46. Spark should enforce a schema according to the schema shown below. Find the error.
Schema:
1.root
2. |-- itemId: integer (nullable = true)
3. |-- attributes: array (nullable = true)
4. | |-- element: string (containsNull = true)
5. |-- supplier: string (nullable = true)
Code block:
1.schema = StructType([
2. StructType("itemId", IntegerType(), True),
3. StructType("attributes", ArrayType(StringType(), True), True),
4. StructType("supplier", StringType(), True)
5.])
6.
7.spark.read.options("modifiedBefore", "2029-03-20T05:44:46").schema(schema).load(filePath)

• A. The data type of the schema is incompatible with the schema() operator and the modification date threshold is specified incorrectly.
• B. The attributes array is specified incorrectly, Spark cannot identify the file format, and the syntax of the call to Spark's DataFrameReader is incorrect.
• C. Columns in the schema definition use the wrong object type, the modification date threshold is specified incorrectly, and Spark cannot identify the file format.
• D. Columns in the schema definition use the wrong object type and the syntax of the call to Spark's DataFrameReader is incorrect.
• E. Columns in the schema are unable to handle empty values and the modification date threshold is specified incorrectly.

Answer: C

Explanation:
Explanation
Correct code block:
schema = StructType([
StructField("itemId", IntegerType(), True),
StructField("attributes", ArrayType(StringType(), True), True),
StructField("supplier", StringType(), True)
])
spark.read.options(modifiedBefore="2029-03-20T05:44:46").schema(schema).parquet(filePath) This question is more difficult than what you would encounter in the exam. In the exam, for this question type, only one error needs to be identified and not "one or multiple" as in the question.
Columns in the schema definition use the wrong object type, the modification date threshold is specified incorrectly, and Spark cannot identify the file format.
Correct! Columns in the schema definition should use the StructField type. Building a schema from pyspark.sql.types, as here using classes like StructType and StructField, is one of multiple ways of expressing a schema in Spark. A StructType always contains a list of StructFields (see documentation linked below). So, nesting StructType and StructType as shown in the question is wrong.
The modification date threshold should be specified by a keyword argument like options(modifiedBefore="2029-03-20T05:44:46") and not two consecutive non-keyword arguments as in the original code block (see documentation linked below).
Spark cannot identify the file format correctly, because either it has to be specified by using the DataFrameReader.format(), as an argument to DataFrameReader.load(), or directly by calling, for example, DataFrameReader.parquet().
Columns in the schema are unable to handle empty values and the modification date threshold is specified incorrectly.
No. If StructField would be used for the columns instead of StructType (see above), the third argument specified whether the column is nullable. The original schema shows that columns should be nullable and this is specified correctly by the third argument being True in the schema in the code block.
It is correct, however, that the modification date threshold is specified incorrectly (see above).
The attributes array is specified incorrectly, Spark cannot identify the file format, and the syntax of the call to Spark's DataFrameReader is incorrect.
Wrong. The attributes array is specified correctly, following the syntax for ArrayType (see linked documentation below). That Spark cannot identify the file format is correct, see correct answer above. In addition, the DataFrameReader is called correctly through the SparkSession spark.
Columns in the schema definition use the wrong object type and the syntax of the call to Spark's DataFrameReader is incorrect.
Incorrect, the object types in the schema definition are correct and syntax of the call to Spark's DataFrameReader is correct.
The data type of the schema is incompatible with the schema() operator and the modification date threshold is specified incorrectly.
False. The data type of the schema is StructType and an accepted data type for the DataFrameReader.schema() method. It is correct however that the modification date threshold is specified incorrectly (see correct answer above).

NEW QUESTION 56
Which of the elements in the labeled panels represent the operation performed for broadcast variables?
Larger image

• A. 2, 3
• B. 1, 3, 4
• C. 2, 5
• D. 1, 2
• E. 0

Answer: A

Explanation:
Explanation
2,3
Correct! Both panels 2 and 3 represent the operation performed for broadcast variables. While a broadcast operation may look like panel 3, with the driver being the bottleneck, it most probably looks like panel 2.
This is because the torrent protocol sits behind Spark's broadcast implementation. In the torrent protocol, each executor will try to fetch missing broadcast variables from the driver or other nodes, preventing the driver from being the bottleneck.
1,2
Wrong. While panel 2 may represent broadcasting, panel 1 shows bi-directional communication which does not occur in broadcast operations.
3
No. While broadcasting may materialize like shown in panel 3, its use of the torrent protocol also enables communciation as shown in panel 2 (see first explanation).
1,3,4
No. While panel 2 shows broadcasting, panel 1 shows bi-directional communication - not a characteristic of broadcasting. Panel 4 shows uni-directional communication, but in the wrong direction.
Panel 4 resembles more an accumulator variable than a broadcast variable.
2,5
Incorrect. While panel 2 shows broadcasting, panel 5 includes bi-directional communication - not a characteristic of broadcasting.
More info: Broadcast Join with Spark - henning.kropponline.de

NEW QUESTION 57
......

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