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 writes DataFrame itemsDf to disk at storage location filePath, making sure to substitute any existing data at that location?

• A. itemsDf.write().parquet(filePath, mode="overwrite")
• B. itemsDf.write.mode("overwrite").parquet(filePath)
• C. itemsDf.write.option("parquet").mode("overwrite").path(filePath)
• D. itemsDf.write.mode("overwrite").path(filePath)
• E. itemsDf.write(filePath, mode="overwrite")

Which of the following code blocks returns a DataFrame where columns predError and productId are removed from DataFrame transactionsDf?
Sample of DataFrame transactionsDf:
1.+-------------+---------+-----+-------+---------+----+
2.|transactionId|predError|value|storeId|productId|f |
3.+-------------+---------+-----+-------+---------+----+
4.|1 |3 |4 |25 |1 |null|
5.|2 |6 |7 |2 |2 |null|
6.|3 |3 |null |25 |3 |null|
7.+-------------+---------+-----+-------+---------+----+

• A. transactionsDf.drop("predError", "productId", "associateId")
• B. transactionsDf.drop(["predError", "productId", "associateId"])
• C. transactionsDf.withColumnRemoved("predError", "productId")
• D. transactionsDf.drop(col("predError", "productId"))
• E. transactionsDf.dropColumns("predError", "productId", "associateId")

Which of the following code blocks displays various aggregated statistics of all columns in DataFrame transactionsDf, including the standard deviation and minimum of values in each column?

• A. transactionsDf.summary()
• B. transactionsDf.summary("count", "mean", "stddev", "25%", "50%", "75%", "max").show()
• C. transactionsDf.agg("count", "mean", "stddev", "25%", "50%", "75%", "min").show()
• D. transactionsDf.agg("count", "mean", "stddev", "25%", "50%", "75%", "min")
• E. transactionsDf.summary().show()

The code block shown below should return all rows of DataFrame itemsDf that have at least 3 items in column itemNameElements. Choose the answer that correctly fills the blanks in the code block to accomplish this.
Example of DataFrame itemsDf:
1.+------+----------------------------------+-------------------+------------------------------------------+
2.|itemId|itemName |supplier |itemNameElements |
3.+------+----------------------------------+-------------------+------------------------------------------+
4.|1 |Thick Coat for Walking in the Snow|Sports Company Inc.|[Thick, Coat, for, Walking, in, the, Snow]|
5.|2 |Elegant Outdoors Summer Dress |YetiX |[Elegant, Outdoors, Summer, Dress] |
6.|3 |Outdoors Backpack |Sports Company Inc.|[Outdoors, Backpack] |
7.+------+----------------------------------+-------------------+------------------------------------------+ Code block:
itemsDf.__1__(__2__(__3__)__4__)

• A. 1. filter
  2. count
  3. itemNameElements
  4. >=3
• B. 1. select
  2. size
  3. "itemNameElements"
  4. >3
• C. 1. filter
  2. size
  3. "itemNameElements"
  4. >=3
  (Correct)
• D. 1. select
  2. count
  3. "itemNameElements"
  4. >3
• E. 1. select
  2. count
  3. col("itemNameElements")
  4. >3