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()

Drag options to blanks, or click blank then click option'

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)

Drag options to blanks, or click blank then click option'

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])

Drag options to blanks, or click blank then click option'

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])

Drag options to blanks, or click blank then click option'

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}

Drag options to blanks, or click blank then click option'

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.

Practice

1. What is Apache Spark primarily used for?

easy

A. Creating websites and web pages

B. Processing large amounts of data quickly using many computers

C. Designing computer hardware

D. Writing simple text documents

Solution

1. Step 1: Understand Apache Spark's purpose

   Apache Spark is designed to handle big data processing efficiently by using multiple computers working together.

2. Step 2: Compare options with Spark's use

   Options B, C, and D describe unrelated tasks like web design, hardware, or text editing, which Spark does not do.

3. Final Answer:

   Processing large amounts of data quickly using many computers -> Option B

4. Quick Check:

   Big data processing = A [OK]

Hint: Think big data and speed with many computers [OK]

Common Mistakes:

• Confusing Spark with web or hardware tools
• Thinking Spark is for simple text editing

2. Which of the following is the correct way to start a Spark session in Python?

easy

A. spark = SparkSession.builder.appName('App').getOrCreate()

B. spark = SparkSession.create('App')

C. spark = SparkSession.start('App')

D. spark = SparkSession.run('App')

Solution

1. Step 1: Recall Spark session creation syntax

   The correct syntax to create a Spark session is using builder, setting appName, then getOrCreate().

2. Step 2: Check each option's method

   Only spark = SparkSession.builder.appName('App').getOrCreate() uses builder.appName(...).getOrCreate(), others use invalid methods.

3. Final Answer:

   spark = SparkSession.builder.appName('App').getOrCreate() -> Option A

4. Quick Check:

   builder.appName().getOrCreate() = B [OK]

Hint: Remember builder.appName().getOrCreate() pattern [OK]

Common Mistakes:

• Using non-existent methods like create or start
• Missing getOrCreate() call

3. Given the following Spark code, what will be the output?

from pyspark.sql import SparkSession
spark = SparkSession.builder.appName('Test').getOrCreate()
data = [(1, 'apple'), (2, 'banana'), (3, 'cherry')]
df = spark.createDataFrame(data, ['id', 'fruit'])
df.filter(df.id > 1).count()

medium

A. 3

B. 1

C. 0

D. 2

Solution

1. Step 1: Understand the DataFrame content

   The DataFrame has 3 rows with ids 1, 2, and 3.

2. Step 2: Apply the filter condition df.id > 1

   This keeps rows with id 2 and 3, so 2 rows remain.

3. Final Answer:

   2 -> Option D

4. Quick Check:

   Rows with id > 1 = 2 [OK]

Hint: Count rows after filter condition carefully [OK]

Common Mistakes:

• Counting all rows without filter
• Misunderstanding filter condition

4. Identify the error in this Spark code snippet:

from pyspark.sql import SparkSession
spark = SparkSession.builder.appName('Debug').getOrCreate()
data = [(1, 'x'), (2, 'y')]
df = spark.createDataFrame(data, ['num', 'char'])
df.select('number').show()

medium

A. DataFrame creation syntax is wrong

B. SparkSession not created properly

C. Column 'number' does not exist in DataFrame

D. show() method is not valid

Solution

1. Step 1: Check DataFrame columns

   The DataFrame columns are 'num' and 'char', not 'number'.

2. Step 2: Analyze the select statement

   Selecting 'number' column causes an error because it does not exist.

3. Final Answer:

   Column 'number' does not exist in DataFrame -> Option C

4. Quick Check:

   Wrong column name = A [OK]

Hint: Verify column names before selecting [OK]

Common Mistakes:

• Using wrong column names
• Assuming show() is invalid
• Thinking SparkSession creation failed

5. You have a large dataset split across many files. Which Apache Spark feature helps you process this data efficiently in one place?

hard

A. Spark SQL to query data using SQL syntax

B. Spark Streaming for real-time data only

C. Spark MLlib for machine learning only

D. Spark GraphX for graph data only

Solution

1. Step 1: Identify the need to process large datasets in one place

   Spark SQL allows querying structured data from many files as if it were one table.

2. Step 2: Compare other Spark components

   Spark Streaming is for live data, MLlib is for machine learning, GraphX is for graph data, so they don't fit the question.

3. Final Answer:

   Spark SQL to query data using SQL syntax -> Option A

4. Quick Check:

   Query large data with Spark SQL = D [OK]

Hint: Use Spark SQL for unified big data queries [OK]

Common Mistakes:

• Confusing Spark SQL with streaming or ML
• Thinking MLlib or GraphX handle all data types