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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

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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

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

Options:

A.

itemsDf.select(explode("attributes").alias("attributes_exploded")).filter(attributes_exploded.contains("i"))

B.

itemsDf.explode(attributes).alias("attributes_exploded").filter(col("attributes_exploded").contains("i"))

C.

itemsDf.select(explode("attributes")).filter("attributes_exploded".contains("i"))

D.

itemsDf.select(explode("attributes").alias("attributes_exploded")).filter(col("attributes_exploded").contains("i"))

E.

itemsDf.select(col("attributes").explode().alias("attributes_exploded")).filter(col("attributes_exploded").contains("i"))

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.

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