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Databricks Certified Associate Developer for Apache Spark 3.0 Exam Questions and Answers

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Question 1

Which of the following code blocks returns a one-column DataFrame for which every row contains an array of all integer numbers from 0 up to and including the number given in column predError of

DataFrame transactionsDf, and null if predError is null?

Sample of DataFrame transactionsDf:

1.+-------------+---------+-----+-------+---------+----+

3.+-------------+---------+-----+-------+---------+----+

4.| 1| 3| 4| 25| 1|null|

5.| 2| 6| 7| 2| 2|null|

6.| 3| 3| null| 25| 3|null|

7.| 4| null| null| 3| 2|null|

8.| 5| null| null| null| 2|null|

9.| 6| 3| 2| 25| 2|null|

10.+-------------+---------+-----+-------+---------+----+

Options:

1.def count_to_target(target):

2. if target is None:

3. return

5. result = [range(target)]

6. return result

8.count_to_target_udf = udf(count_to_target, ArrayType[IntegerType])

10.transactionsDf.select(count_to_target_udf(col('predError')))

1.def count_to_target(target):

2. if target is None:

3. return

5. result = list(range(target))

6. return result

8.transactionsDf.select(count_to_target(col('predError')))

1.def count_to_target(target):

2. if target is None:

3. return

5. result = list(range(target))

6. return result

8.count_to_target_udf = udf(count_to_target, ArrayType(IntegerType()))

10.transactionsDf.select(count_to_target_udf('predError'))

(Correct)

1.def count_to_target(target):

2. result = list(range(target))

3. return result

5.count_to_target_udf = udf(count_to_target, ArrayType(IntegerType()))

7.df = transactionsDf.select(count_to_target_udf('predError'))

1.def count_to_target(target):

2. if target is None:

3. return

5. result = list(range(target))

6. return result

8.count_to_target_udf = udf(count_to_target)

10.transactionsDf.select(count_to_target_udf('predError'))

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Question 2

In which order should the code blocks shown below be run in order to create a table of all values in column attributes next to the respective values in column supplier in DataFrame itemsDf?

1. itemsDf.createOrReplaceView("itemsDf")

2. spark.sql("FROM itemsDf SELECT 'supplier', explode('Attributes')")

3. spark.sql("FROM itemsDf SELECT supplier, explode(attributes)")

4. itemsDf.createOrReplaceTempView("itemsDf")

Options:

4, 3

1, 3

4, 2

1, 2

Question 3

Which of the following code blocks produces the following output, given DataFrame transactionsDf?

Output:

1.root

2. |-- transactionId: integer (nullable = true)

3. |-- predError: integer (nullable = true)

4. |-- value: integer (nullable = true)

5. |-- storeId: integer (nullable = true)

6. |-- productId: integer (nullable = true)

7. |-- f: integer (nullable = true)

DataFrame transactionsDf:

1.+-------------+---------+-----+-------+---------+----+

3.+-------------+---------+-----+-------+---------+----+

4.| 1| 3| 4| 25| 1|null|

5.| 2| 6| 7| 2| 2|null|

6.| 3| 3| null| 25| 3|null|

7.+-------------+---------+-----+-------+---------+----+

Options:

transactionsDf.schema.print()

transactionsDf.rdd.printSchema()

transactionsDf.rdd.formatSchema()

transactionsDf.printSchema()

print(transactionsDf.schema)

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