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

Options:

A.

transactionsDf.withColumnRemoved("predError", "productId")

B.

transactionsDf.drop(["predError", "productId", "associateId"])

C.

transactionsDf.drop("predError", "productId", "associateId")

D.

transactionsDf.dropColumns("predError", "productId", "associateId")

E.

transactionsDf.drop(col("predError", "productId"))

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Question 2

The code block displayed below contains an error. The code block should count the number of rows that have a predError of either 3 or 6. Find the error.

Code block:

transactionsDf.filter(col('predError').in([3, 6])).count()

Options:

A.

The number of rows cannot be determined with the count() operator.

B.

Instead of filter, the select method should be used.

C.

The method used on column predError is incorrect.

D.

Instead of a list, the values need to be passed as single arguments to the in operator.

E.

Numbers 3 and 6 need to be passed as string variables.

Question 3

Which of the following code blocks shuffles DataFrame transactionsDf, which has 8 partitions, so that it has 10 partitions?

Options:

A.

transactionsDf.repartition(transactionsDf.getNumPartitions()+2)

B.

transactionsDf.repartition(transactionsDf.rdd.getNumPartitions()+2)

C.

transactionsDf.coalesce(10)

D.

transactionsDf.coalesce(transactionsDf.getNumPartitions()+2)

E.

transactionsDf.repartition(transactionsDf._partitions+2)