Why pipeline design determines data platform quality in Apache Spark - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

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

Apache Spark

df = spark.read.[1]("data/input.csv")

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

Acsv

Bjson

Cparquet

Dtext

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'json' or 'parquet' to read a CSV file causes errors.

Forgetting to specify the correct read method.

The csv method reads CSV files into a DataFrame.

2fill in blank

medium

Complete 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'

Adf['age']

Bage

Cdf.age

D"age"

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the column without quotes causes errors.

Using DataFrame attribute instead of string.

The select method requires column names as strings.

3fill in blank

hard

Fix the error in filtering rows where 'score' is greater than 50.

Apache Spark

filtered = df.filter(df.score [1] 50)

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

A>

B==

C=

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using '=' instead of '>' causes syntax errors.

Using '==' filters only equal values, not greater.

Use > to filter rows with score greater than 50.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths for words longer than 3 characters.

Apache Spark

lengths = {word: [1] for word in words if [2]

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

Alen(word)

Blen(word) > 3

Cword

Dword > 3

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'word > 3' compares strings to numbers, causing errors.

Not using length function for mapping.

The dictionary maps each word to its length, filtering words longer than 3 characters.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their scores if the score is positive.

Apache Spark

result = [1]: [2] for word, score in data.items() if score [3] 0}

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

Aword.upper()

Bscore

C>

Dword.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using word.lower() instead of uppercase.

Using '<' instead of '>' in the condition.

The dictionary maps uppercase words to their positive scores.

Practice

1. Why is good pipeline design important for a data platform in Apache Spark?

easy

A. It slows down data processing to avoid errors.

B. It increases the size of the data stored.

C. It makes the data platform reliable and easier to maintain.

D. It removes the need for data cleaning.

Solution

1. Step 1: Understand pipeline design purpose

   Good pipeline design organizes data processing into clear steps, improving reliability and maintenance.

2. Step 2: Evaluate options

   Only It makes the data platform reliable and easier to maintain. correctly states the benefit of good pipeline design; others are incorrect or unrelated.

3. Final Answer:

   It makes the data platform reliable and easier to maintain. -> Option C

4. Quick Check:

   Pipeline design = reliability and maintenance [OK]

Hint: Good pipelines improve reliability and maintenance [OK]

Common Mistakes:

• Thinking pipelines increase data size
• Believing pipelines slow down processing
• Assuming pipelines remove data cleaning

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

easy

A. spark.read.csv('data.csv').filter('age > 30')

B. spark.load('data.csv').filter(age > 30)

C. spark.read.csv('data.csv').filter(age > 30)

D. spark.read.csv('data.csv').filter('age > 30')()

Solution

1. Step 1: Identify correct Spark syntax for reading and filtering

   In Spark, reading CSV uses spark.read.csv(), and filtering uses a string expression or column object.

2. Step 2: Check each option

   spark.read.csv('data.csv').filter('age > 30') uses correct syntax with string filter; B uses non-existent 'load'; C misses quotes around condition; A adds invalid parentheses after filter.

3. Final Answer:

   spark.read.csv('data.csv').filter('age > 30') -> Option A

4. Quick Check:

   Correct Spark read and filter syntax = spark.read.csv('data.csv').filter('age > 30') [OK]

Hint: Use spark.read.csv() and filter with string condition [OK]

Common Mistakes:

• Using spark.load instead of spark.read.csv
• Missing quotes around filter condition
• Adding extra parentheses after filter

3. Given this Spark pipeline code, what is the output count?

df = spark.createDataFrame([(1, 'A'), (2, 'B'), (3, 'C')], ['id', 'category'])
filtered = df.filter("id > 1")
count = filtered.count()

medium

A. 2

B. 3

C. 1

D. 0

Solution

1. Step 1: Understand the DataFrame content

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

2. Step 2: Apply filter condition

   Filter keeps rows where id > 1, so rows with id 2 and 3 remain (2 rows).

3. Final Answer:

   2 -> Option A

4. Quick Check:

   Rows with id > 1 = 2 [OK]

Hint: Count rows after filter condition carefully [OK]

Common Mistakes:

• Counting all rows without filtering
• Misunderstanding filter condition syntax
• Assuming filter includes id = 1

4. Identify the error in this Spark pipeline code snippet:

df = spark.read.csv('data.csv')
filtered = df.filter(age > 25)
filtered.show()

medium

A. Incorrect method name 'read.csv'.

B. Using show() before filter.

C. No error; code runs fine.

D. Missing quotes around 'age > 25' in filter.

Solution

1. Step 1: Check filter syntax

   Filter expects a string condition or column expression; here, age > 25 is unquoted, causing error.

2. Step 2: Verify other parts

   read.csv is correct; show() after filter is valid; only missing quotes cause error.

3. Final Answer:

   Missing quotes around 'age > 25' in filter. -> Option D

4. Quick Check:

   Filter condition must be string or column [OK]

Hint: Always quote filter conditions or use column objects [OK]

Common Mistakes:

• Forgetting quotes in filter condition
• Thinking show() must come before filter
• Confusing read.csv with other methods

5. You want to design a Spark pipeline that reads raw data, cleans it by removing nulls, and then aggregates sales by region. Which pipeline design approach best ensures quality and maintainability?

hard

A. Combine all steps in one long chain without intermediate variables.

B. Separate each step into its own clearly named DataFrame variable.

C. Write all transformations inside a single SQL query string.

D. Process data manually outside Spark before loading.

Solution

1. Step 1: Understand pipeline design goals

   Good design means clear, maintainable steps that are easy to debug and update.

2. Step 2: Evaluate options for clarity and maintainability

   Separate each step into its own clearly named DataFrame variable. separates steps with clear names, improving readability and maintenance. Long chains without intermediate variables and single SQL queries make debugging harder; processing data manually outside Spark ignores its benefits.

3. Final Answer:

   Separate each step into its own clearly named DataFrame variable. -> Option B

4. Quick Check:

   Clear step separation = maintainable pipeline [OK]

Hint: Use separate variables for each pipeline step [OK]

Common Mistakes:

• Chaining all steps without clarity
• Using complex single SQL strings
• Avoiding Spark transformations