Pass with professional Associate-Developer-Apache-Spark actual quiz materials

Which of the following code blocks reads in the JSON file stored at filePath, enforcing the schema expressed in JSON format in variable json_schema, shown in the code block below?
Code block:
1.json_schema = """
2.{"type": "struct",
3. "fields": [
4. {
5. "name": "itemId",
6. "type": "integer",
7. "nullable": true,
8. "metadata": {}
9. },
10. {
11. "name": "supplier",
12. "type": "string",
13. "nullable": true,
14. "metadata": {}
15. }
16. ]
17.}
18."""

• A. spark.read.schema(json_schema).json(filePath)
  1.schema = StructType.fromJson(json.loads(json_schema))
  2.spark.read.json(filePath, schema=schema)
• B. spark.read.json(filePath, schema=spark.read.json(json_schema))
• C. spark.read.json(filePath, schema=schema_of_json(json_schema))
• D. spark.read.json(filePath, schema=json_schema)

Which of the following code blocks returns a DataFrame with a single column in which all items in column attributes of DataFrame itemsDf are listed that contain the letter i?
Sample of DataFrame itemsDf:
1.+------+----------------------------------+-----------------------------+-------------------+
2.|itemId|itemName |attributes |supplier |
3.+------+----------------------------------+-----------------------------+-------------------+
4.|1 |Thick Coat for Walking in the Snow|[blue, winter, cozy] |Sports Company Inc.|
5.|2 |Elegant Outdoors Summer Dress |[red, summer, fresh, cooling]|YetiX |
6.|3 |Outdoors Backpack |[green, summer, travel] |Sports Company Inc.|
7.+------+----------------------------------+-----------------------------+-------------------+

• A. itemsDf.select(col("attributes").explode().alias("attributes_exploded")).filter(col("attributes_exploded").co
• B. itemsDf.select(explode("attributes").alias("attributes_exploded")).filter(col("attributes_exploded").contain
• C. itemsDf.select(explode("attributes").alias("attributes_exploded")).filter(attributes_exploded.contains("i"))
• D. itemsDf.explode(attributes).alias("attributes_exploded").filter(col("attributes_exploded").contains("i"))
• E. itemsDf.select(explode("attributes")).filter("attributes_exploded".contains("i"))

Which of the following code blocks returns a copy of DataFrame transactionsDf in which column productId has been renamed to productNumber?

• A. transactionsDf.withColumnRenamed("productId", "productNumber")
• B. transactionsDf.withColumnRenamed(productId, productNumber)
• C. transactionsDf.withColumnRenamed(col(productId), col(productNumber))
• D. transactionsDf.withColumn("productId", "productNumber")
• E. transactionsDf.withColumnRenamed("productNumber", "productId")

Which of the following describes a shuffle?

• A. A shuffle is a process that compares data across executors.
• B. A shuffle is a process that compares data across partitions.
• C. A shuffle is a process that allocates partitions to executors.
• D. A shuffle is a process that is executed during a broadcast hash join.
• E. A shuffle is a Spark operation that results from DataFrame.coalesce().

The code block displayed below contains an error. The code block should merge the rows of DataFrames transactionsDfMonday and transactionsDfTuesday into a new DataFrame, matching column names and inserting null values where column names do not appear in both DataFrames. Find the error.
Sample of DataFrame transactionsDfMonday:
1.+-------------+---------+-----+-------+---------+----+
2.|transactionId|predError|value|storeId|productId| f|
3.+-------------+---------+-----+-------+---------+----+
4.| 5| null| null| null| 2|null|
5.| 6| 3| 2| 25| 2|null|
6.+-------------+---------+-----+-------+---------+----+
Sample of DataFrame transactionsDfTuesday:
1.+-------+-------------+---------+-----+
2.|storeId|transactionId|productId|value|
3.+-------+-------------+---------+-----+
4.| 25| 1| 1| 4|
5.| 2| 2| 2| 7|
6.| 3| 4| 2| null|
7.| null| 5| 2| null|
8.+-------+-------------+---------+-----+
Code block:
sc.union([transactionsDfMonday, transactionsDfTuesday])

• A. Instead of the Spark context, transactionDfMonday should be called with the union method.
• B. Instead of the Spark context, transactionDfMonday should be called with the unionByName method instead of the union method, making sure to not use its default arguments.
• C. The DataFrames' RDDs need to be passed into the sc.union method instead of the DataFrame variable names.
• D. Instead of union, the concat method should be used, making sure to not use its default arguments.
• E. Instead of the Spark context, transactionDfMonday should be called with the join method instead of the union method, making sure to use its default arguments.