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

Become Databricks Certified with updated Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 exam questions and correct answers

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Total 420 Questions | Updated On: Dec 14, 2021

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Question 1

Which of the following 3 DataFrame operations are NOT classified as an action? Choose 3 answers:

A : reach()

B : che()

C : mit()

D : intSchema()

E : ow()

F : rst()

Answer: B,C,D

Question 2

Which of the following code blocks reads in parquet file /FileStore/imports.parquet as a DataFrame?

A : ark.mode("parquet").read("/FileStore/imports.parquet")

B : ark.read.path("/FileStore/imports.parquet", source="parquet")

C : ark.read().parquet("/FileStore/imports.parquet")

D : ark.read.parquet("/FileStore/imports.parquet")

E : ark.read().format('parquet').open("/FileStore/imports.parquet")

Answer: D

Question 3

When the property spark.sql.optimizer.dynamicPartitionPruning.enabled is set to true, what optimization happens in spark ?

A : allows you to dynamically switch join strategies.

B : ark dynamically handles skew in sort-merge join by splitting (and replicating if needed) skewed partitions with this property enabled.

C : u to read only as much data as you need.

D : allows you to dynamically select physical plans based on cost.

Answer: C

Question 4

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 5

The code block shown below should return a DataFrame with two columns, itemId and col. In this DataFrame,

for each element in column attributes of DataFrame itemDf there should be a separate

row in which the column itemId contains the associated itemId from DataFrame itemsDf. The new DataFrame

should only contain rows for rows in DataFrame itemsDf in which the column attributes

contains the element cozy.

A sample of DataFrame itemsDf is below.

Code block:

itemsDf.__1__(__2__).__3__(__4__, __5__(__6__))

A : 1. filter2. array_contains("cozy")3. select4. "itemId"5. explode6. "attributes"

B : 1. where2. "array_contains(attributes, 'cozy')"3. select4. itemId5. explode6. attributes

C : 1. filter2. "array_contains(attributes, 'cozy')"3. select4. "itemId"5. map6. "attributes"

D : 1. filter2. "array_contains(attributes, cozy)"3. select4. "itemId"5. explode6. "attributes"

E : 1. filter2. "array_contains(attributes, 'cozy')"3. select4. "itemId"5. explode6. "attributes"

Answer: A

Page: 1 / 84

Total 420 Questions | Updated On: Dec 14, 2021

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