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).

Explanation
Use a narrow transformation to reduce the number of partitions.
Correct! DataFrame.coalesce(n) is a narrow transformation, and in fact the most efficient way to resize the DataFrame of all options listed. One would run DataFrame.coalesce(8) to resize the DataFrame.
Use operation DataFrame.coalesce(8) to fully shuffle the DataFrame and reduce the number of partitions.
Wrong. The coalesce operation avoids a full shuffle, but will shuffle data if needed. This answer is incorrect because it says "fully shuffle" - this is something the coalesce operation will not do. As a general rule, it will reduce the number of partitions with the very least movement of data possible. More info:
distributed computing - Spark - repartition() vs coalesce() - Stack Overflow Use operation DataFrame.coalesce(0.5) to halve the number of partitions in the DataFrame.
Incorrect, since the num_partitions parameter needs to be an integer number defining the exact number of partitions desired after the operation. More info: pyspark.sql.DataFrame.coalesce - PySpark 3.1.2 documentation Use operation DataFrame.repartition(8) to shuffle the DataFrame and reduce the number of partitions.
No. The repartition operation will fully shuffle the DataFrame. This is not the most efficient way of reducing the number of partitions of all listed options.
Use a wide transformation to reduce the number of partitions.
No. While possible via the DataFrame.repartition(n) command, the resulting full shuffle is not the most efficient way of reducing the number of partitions.

Explanation
More info: pyspark.sql.DataFrame.withColumnRenamed - PySpark 3.1.2 documentation Static notebook | Dynamic notebook: See test 2

Explanation
Correct code block:
accum=sc.accumulator(0)
def check_if_inc_in_supplier(row):
if 'Inc.' in row['supplier']:
accum.add(1)
itemsDf.foreach(check_if_inc_in_supplier)
print(accum.value)
To answer this question correctly, you need to know both about the DataFrame.foreach() method and accumulators.
When Spark runs the code, it executes it on the executors. The executors do not have any information about variables outside of their scope. This is whhy simply using a Python variable counter, like in the two examples that start with counter = 0, will not work. You need to tell the executors explicitly that counter is a special shared variable, an Accumulator, which is managed by the driver and can be accessed by all executors for the purpose of adding to it.
If you have used Pandas in the past, you might be familiar with the iterrows() command. Notice that there is no such command in PySpark.
The two examples that start with print do not work, since DataFrame.foreach() does not have a return value.
More info: pyspark.sql.DataFrame.foreach - PySpark 3.1.2 documentation
Static notebook | Dynamic notebook: See test 3