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Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 PDF + Testing Engine



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  • Exam Name: Databricks Certified Associate Developer for Apache Spark 3.0 Exam
  • Last Update: 19-Mar-2023
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Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 Questions and Answers

Question # 1

Which of the following describes Spark's way of managing memory?


Spark uses a subset of the reserved system memory.


Storage memory is used for caching partitions derived from DataFrames.


As a general rule for garbage collection, Spark performs better on many small objects than few big objects.


Disabling serialization potentially greatly reduces the memory footprint of a Spark application.


Spark's memory usage can be divided into three categories: Execution, transaction, and storage.

Question # 2

Which of the following describes the role of the cluster manager?


The cluster manager schedules tasks on the cluster in client mode.


The cluster manager schedules tasks on the cluster in local mode.


The cluster manager allocates resources to Spark applications and maintains the executor processes in client mode.


The cluster manager allocates resources to Spark applications and maintains the executor processes in remote mode.


The cluster manager allocates resources to the DataFrame manager.

Question # 3

Which of the following code blocks creates a new DataFrame with 3 columns, productId, highest, and lowest, that shows the biggest and smallest values of column value per value in column

productId from DataFrame transactionsDf?

Sample of DataFrame transactionsDf:


2.|transactionId|predError|value|storeId|productId| f|


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|





transactionsDf.agg(max('value').alias('highest'), min('value').alias('lowest'))


transactionsDf.groupby(col(productId)).agg(max(col(value)).alias("highest"), min(col(value)).alias("lowest"))


transactionsDf.groupby('productId').agg(max('value').alias('highest'), min('value').alias('lowest'))


transactionsDf.groupby("productId").agg({"highest": max("value"), "lowest": min("value")})

Question # 4

The code block displayed below contains an error. The code block is intended to return all columns of DataFrame transactionsDf except for columns predError, productId, and value. Find the error.

Excerpt of DataFrame transactionsDf:"predError"), ~col("productId"), ~col("value"))


The select operator should be replaced by the drop operator and the arguments to the drop operator should be column names predError, productId and value wrapped in the col operator so they

should be expressed like drop(col(predError), col(productId), col(value)).


The select operator should be replaced with the deselect operator.


The column names in the select operator should not be strings and wrapped in the col operator, so they should be expressed like select(~col(predError), ~col(productId), ~col(value)).


The select operator should be replaced by the drop operator.


The select operator should be replaced by the drop operator and the arguments to the drop operator should be column names predError, productId and value as strings.


Question # 5

Which of the elements that are labeled with a circle and a number contain an error or are misrepresented?


1, 10


1, 8




7, 9, 10


1, 4, 6, 9

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