Free Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 Exam Questions - Easiest Way for Success

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Total 420 Questions | Updated On: Sep 13, 2024

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Question 1

There is a global temp view named ‘my_global_view’. If I want to query this view within spark, which command do we need to choose ?

A : spark.read.table("global_temp.my_global_view")

B : spark.read.view("global_temp.my_global_view")

C : spark.read.view("my_global_view")

D : spark.read.table("my_global_view")

Answer: A

Question 2

Choose the correct code block to unpersist a table named ‘table’.

A : spark.catalog.uncacheTable(table)

B : spark.uncacheTable(table)

C : spark.catalog.uncacheTable(“table”)

D : spark.uncacheTable(“table”)

Answer: C

Question 3

Given an instance of SparkSession named spark, reviewing the following code what's the output ?

1. from pyspark.sql.types import IntegerType

2. from pyspark.sql.functions import col

3. import pyspark.sql.functions as f

6. a = [1002, 3001, 4002, 2003, 2002, 3004, 1003, 4006]

7. b = spark

8. .createDataFrame(a, IntegerType())

9. .withColumn("x", col("value") 00)

10.

11. c = b

12. .groupBy(col("x"))

13. .agg(f.count("x"), f.sum("value"))

14. .drop("x")

15. .toDF("count", "total")

16. .orderBy(col("count").desc(), col("total"))

17. .limit(1)

18. .show()

A : 1. |count|total|2. | 2| 8008|

B : 1. |count|total|2. | 3| 7006|

C : 1. |count|total|2. | 8|20023|

D : 1. |count|total|2. | 1| 3001|

Answer: B

Question 4

Which of the following code blocks stores a part of the data in DataFrame itemsDf on executors?

A : itemsDf.cache().count()

B : itemsDf.cache(eager=True)

C : cache(itemsDf)

D : itemsDf.cache().filter()

E : itemsDf.rdd.storeCopy()

Answer: A

Question 5

Which of the following code blocks reads from a csv file where values are separated with ‘;’ ?

A : spark.read.format("csv").option("header", "true").option(“inferSchema”, “true”).option(“sep”, “true”).toDf(file)

B : spark.read.format("csv").option("header", "true").option(“inferSchema”, “true”).load(file)

C : spark.read.format("csv").option("header", "true").option(“inferSchema”, “true”).option(“sep”, “;”).load(file)

D : spark.load.format("csv").option("header", "true").option(“inferSchema”, “true”).read(file)

Answer: C

Page: 1 / 84

Total 420 Questions | Updated On: Sep 13, 2024

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