Question 1

Which property is used to to allocates jobs to different resource pools to achieve resources scheduling within an application ?

Options :

A : Fair Scheduler

B : Dynamic allocation

C : There is no need to set a property since spark is by default capable of resizing

Answer: A

Question 2

Which of the following code blocks reads all CSV files in directory filePath into a single DataFrame, with

column names defined in the CSV file headers?

Content of directory filePath:

1._SUCCESS

2._committed_2754546451699747124

3._started_2754546451699747124

4.part-00000-tid-2754546451699747124-10eb85bf-8d91-4dd0-b60b-2f3c02eeecaa-298-1-c000.csv.gz

5.part-00001-tid-2754546451699747124-10eb85bf-8d91-4dd0-b60b-2f3c02eeecaa-299-1-c000.csv.gz

6.part-00002-tid-2754546451699747124-10eb85bf-8d91-4dd0-b60b-2f3c02eeecaa-300-1-c000.csv.gz

7.part-00003-tid-2754546451699747124-10eb85bf-8d91-4dd0-b60b-2f3c02eeecaa-301-1-c000.csv.gz

spark.option("header",True).csv(filePath)

Options :

A : spark.read.format("csv").option("header",True).option("compression","zip").load(filePath)

B : spark.read().option("header",True).load(filePath)

C : spark.read.format("csv").option("header",True).load(filePath)

D : spark.read.load(filePath)

Answer: C

Question 3

You have a need to transform a column named ‘date’ to a timestamp format. Assume that the column ‘date’ is timestamp compatible. You have written the code block

down below, but it contains an error. Identify and fix it.

df.select(to_timestamp("yyyy-dd-MM", col("date"))).show()

Options :

A :

to_timestamp() is not a valid operation. Proper function is toTimestamp()

1. df.select(toTimestamp(col("date"), "yyyy-dd-MM"))

B :

to_timestamp() is not a valid operation. Proper function is toTimestamp()

1. df.select(toTimestamp("yyyy-dd-MM", col("date")))

C :

No need for the quotes.

df.select(to_timestamp(yyyy-dd-MM, col(date)))

D :

First parameter should be column and then it should be followed by the desired format.

df.select(to_timestamp(col("date"), "yyyy-dd-MM"))

Answer: D

Question 4

Which of the following code blocks reorders the values inside the arrays in column attributes of DataFrame

itemsDf from last to first one in the alphabet?

1.+------+-----------------------------+-------------------+

2.|itemId|attributes |supplier |

3.+------+-----------------------------+-------------------+

4.|1 |[blue, winter, cozy] |Sports Company Inc.|

5.|2 |[red, summer, fresh, cooling]|YetiX |

6.|3 |[green, summer, travel] |Sports Company Inc.|

7.+------+-----------------------------+-------------------+

Options :

A : itemsDf.withColumn('attributes', sort_array(col('attributes').desc()))

B : itemsDf.withColumn('attributes', sort_array(desc('attributes')))

C : itemsDf.withColumn('attributes', sort(col('attributes'), asc=False))

D : itemsDf.withColumn("attributes", sort_array("attributes", asc=False))

E : itemsDf.select(sort_array("attributes"))

Answer: D

Question 5

Which of the following statements about lazy evaluation is incorrect?

Options :

A : Predicate pushdown is a feature resulting from lazy evaluation.

B : Execution is triggered by transformations.

C : Spark will fail a job only during execution, but not during definition.

D : Accumulators do not change the lazy evaluation model of Spark.

E : Lineages allow Spark to coalesce transformations into stages

Answer: B

Confidently Practice Online with Free Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 Exam Cram

Buying Options: