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Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 Exam Dumps : Databricks Certified Associate Developer for Apache Spark 3.0 Exam

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

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.+-------------+---------+-----+-------+---------+----+

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

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

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

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

Options:

A.

1.def count_to_target(target):

2. if target is None:

3. return

4.

5. result = [range(target)]

6. return result

7.

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

9.

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

B.

1.def count_to_target(target):

2. if target is None:

3. return

4.

5. result = list(range(target))

6. return result

7.

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

C.

1.def count_to_target(target):

2. if target is None:

3. return

4.

5. result = list(range(target))

6. return result

7.

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

9.

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

(Correct)

D.

1.def count_to_target(target):

2. result = list(range(target))

3. return result

4.

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

6.

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

E.

1.def count_to_target(target):

2. if target is None:

3. return

4.

5. result = list(range(target))

6. return result

7.

8.count_to_target_udf = udf(count_to_target)

9.

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

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Question 2

The code block displayed below contains an error. The code block should return a DataFrame where all entries in column supplier contain the letter combination et in this order. Find the error.

Code block:

itemsDf.filter(Column('supplier').isin('et'))

Options:

A.

The Column operator should be replaced by the col operator and instead of isin, contains should be used.

B.

The expression inside the filter parenthesis is malformed and should be replaced by isin('et', 'supplier').

C.

Instead of isin, it should be checked whether column supplier contains the letters et, so isin should be replaced with contains. In addition, the column should be accessed using col['supplier'].

D.

The expression only returns a single column and filter should be replaced by select.

Question 3

Which of the following statements about DAGs is correct?

Options:

A.

DAGs help direct how Spark executors process tasks, but are a limitation to the proper execution of a query when an executor fails.

B.

DAG stands for "Directing Acyclic Graph".

C.

Spark strategically hides DAGs from developers, since the high degree of automation in Spark means that developers never need to consider DAG layouts.

D.

In contrast to transformations, DAGs are never lazily executed.

E.

DAGs can be decomposed into tasks that are executed in parallel.