What is Apache Spark - Interactive Quiz & Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a Spark session.

Apache Spark

from pyspark.sql import SparkSession
spark = SparkSession.builder.appName([1]).getOrCreate()

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AMyApp

B"MyApp"

C123

Dspark

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting to put the app name in quotes.

Using a variable name without quotes.

The appName method expects a string with the application name, so it must be in quotes.

2fill in blank

medium

Complete the code to read a CSV file into a Spark DataFrame.

Apache Spark

df = spark.read.csv([1], header=True, inferSchema=True)

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A"data.csv"

Bdata

Ccsv_file

Ddata.csv

Attempts:

3 left

💡 Hint

Common Mistakes

Not putting the file name in quotes.

Using a variable name without defining it.

The file path must be a string, so it needs to be in quotes.

3fill in blank

hard

Fix the error in the code to select the 'age' column from the DataFrame.

Apache Spark

ages = df.select([1])

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A"age"

Bdf.age

Cage

Ddf[age]

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the column name without quotes.

Using df.age inside select which is invalid.

The select method expects the column name as a string in quotes.

4fill in blank

hard

Fill both blanks to filter rows where age is greater than 30 and select the 'name' column.

Apache Spark

filtered = df.filter(df.age [1] 30).select([2])

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A>

B"name"

C<

Dage

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' for filtering.

Selecting column without quotes.

Use '>' to filter ages greater than 30 and select the 'name' column as a string.

5fill in blank

hard

Fill all three blanks to create a dictionary with column names as keys and their data types as values.

Apache Spark

schema_dict = [1]: [2] for [3] in df.schema.fields}

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Afield.name

Bfield.dataType

Cfield

D{

Attempts:

3 left

💡 Hint

Common Mistakes

Not starting with '{' for dictionary comprehension.

Using wrong variable name in the loop.

Mixing up field.name and field.dataType.

Use a dictionary comprehension starting with '{', iterate over 'field' in schema.fields, and map field.name to field.dataType.