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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

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:

A.

4, 3

B.

1, 3

C.

2

D.

4, 2

E.

1, 2

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Question 2

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.

Question 3

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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