Why data quality prevents downstream failures in Apache Spark - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

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

Apache Spark

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

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

Atext

Bcsv

Cparquet

Djson

Attempts:

3 left

💡 Hint

Common Mistakes

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

Using 'text' reads lines as strings, not structured data.

The csv method reads CSV files into a DataFrame.

2fill in blank

medium

Complete the code to drop rows with any null values.

Apache Spark

clean_df = df.[1]()

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

Adropna

Bna.drop

Cfillna

DdropDuplicates

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'fillna' fills nulls but does not remove rows.

Using 'dropDuplicates' removes duplicate rows, not nulls.

The dropna() method removes rows with null values.

3fill in blank

hard

Fix the error in the code to filter rows where 'age' is greater than 18.

Apache Spark

adults = df.filter(df.age [1] 18)

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

A==

B<

C>

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' selects younger people, not adults.

Using '==' selects only age exactly 18.

The filter should keep rows where age is greater than 18, so use >.

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 3 characters.

Apache Spark

lengths = {word: [1] for word in words if len(word) [2] 3}

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

Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' selects words shorter than 3 characters.

Using 'word' as value stores the word, not its length.

The dictionary maps each word to its length, but only for words longer than 3 characters.

5fill in blank

hard

Fill all three blanks to create a filtered dictionary with uppercase keys and values greater than 0.

Apache Spark

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

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

Ak.upper()

Bv

C>

Dk

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'k' instead of 'k.upper()' keeps keys lowercase.

Using '<' or '==' filters wrong values.

The dictionary comprehension uses uppercase keys, original values, and filters values greater than zero.

Practice

1. Why is maintaining good data quality important in Apache Spark pipelines?

easy

A. It prevents errors and failures in later processing steps

B. It makes the data files smaller in size

C. It speeds up the Spark installation process

D. It automatically creates visualizations

Solution

1. Step 1: Understand the role of data quality

   Good data quality means data is accurate, complete, and consistent, which helps avoid problems later.

2. Step 2: Connect data quality to pipeline stability

   If data has errors or missing values, Spark jobs can fail or produce wrong results downstream.

3. Final Answer:

   It prevents errors and failures in later processing steps -> Option A

4. Quick Check:

   Good data quality = fewer downstream errors [OK]

Hint: Good data quality stops errors later [OK]

Common Mistakes:

• Thinking data size reduction is main goal
• Confusing data quality with installation speed
• Assuming data quality creates visualizations

2. Which Spark DataFrame method is used to remove rows with missing values to improve data quality?

easy

A. fillna()

B. dropna()

C. select()

D. groupBy()

Solution

1. Step 1: Identify method to remove missing data

   dropna() removes rows with null or missing values from a DataFrame.

2. Step 2: Differentiate from other methods

   fillna() fills missing values but does not remove rows; select() chooses columns; groupBy() aggregates data.

3. Final Answer:

   dropna() -> Option B

4. Quick Check:

   dropna() removes missing rows [OK]

Hint: dropna() removes missing rows [OK]

Common Mistakes:

• Confusing fillna() with dropna()
• Using select() to remove rows
• Thinking groupBy() cleans missing data

3. Given this Spark code snippet:

df = spark.createDataFrame([(1, 'Alice'), (None, 'Bob'), (3, None)], ['id', 'name'])
df_clean = df.dropna()
df_clean.show()

What will be the output?

medium

A. +----+-----+
| id | name|
+----+-----+
|null| Bob |
+----+-----+

B. +----+-----+
| id | name|
+----+-----+
| 1 |Alice|
| 3 | null|
+----+-----+

C. +---+-----+
| id| name|
+---+-----+
| 1|Alice|
+---+-----+

D. Error: dropna() is not a valid method

Solution

1. Step 1: Understand dropna() effect on DataFrame

   dropna() removes any row with null in any column.

2. Step 2: Apply dropna() to given data

   Rows (None, 'Bob') and (3, None) have nulls, so only (1, 'Alice') remains.

3. Final Answer:

   +---+-----+ | id| name| +---+-----+ | 1|Alice| +---+-----+ -> Option C

4. Quick Check:

   dropna() removes rows with nulls [OK]

Hint: dropna() removes any row with null [OK]

Common Mistakes:

• Expecting rows with null to remain
• Confusing dropna() with fillna()
• Thinking dropna() causes error

4. You run this code but get an error:

df = spark.createDataFrame([(1, 'Alice'), (2, None)], ['id', 'name'])
df_clean = df.dropna(how='anyy')
df_clean.show()

What is the cause of the error?

medium

A. The 'how' parameter value 'anyy' is invalid

B. dropna() cannot be used on DataFrames

C. The DataFrame has no missing values

D. show() method is not available on DataFrames

Solution

1. Step 1: Check dropna() parameters

   dropna() accepts 'how' parameter with values 'any' or 'all' only.

2. Step 2: Identify invalid parameter usage

   Using 'anyy' causes a runtime error because it's not a valid option.

3. Final Answer:

   The 'how' parameter value 'anyy' is invalid -> Option A

4. Quick Check:

   Invalid parameter value causes error [OK]

Hint: Check parameter spelling carefully [OK]

Common Mistakes:

• Thinking dropna() is invalid on DataFrames
• Ignoring typo in 'how' parameter
• Assuming show() causes error

5. You have a Spark DataFrame with many missing and invalid values. Which approach best prevents downstream failures in your data pipeline?

hard

A. Convert all data to strings to avoid type errors

B. Ignore missing data and handle errors only when they occur downstream

C. Run transformations first, then clean data at the end

D. Remove or fix bad data early using dropna() and fillna() before transformations

Solution

1. Step 1: Understand impact of bad data early

   Bad data causes errors or wrong results if not handled before transformations.

2. Step 2: Choose best practice for data quality

   Removing or fixing missing/invalid data early with dropna() and fillna() prevents failures downstream.

3. Step 3: Evaluate other options

   Ignoring errors delays failure; cleaning last risks wasted work; converting all to strings loses data meaning.

4. Final Answer:

   Remove or fix bad data early using dropna() and fillna() before transformations -> Option D

5. Quick Check:

   Early cleaning prevents downstream failures [OK]

Hint: Clean data early to avoid later errors [OK]

Common Mistakes:

• Delaying cleaning until after transformations
• Ignoring missing data causes hidden errors
• Converting all data to strings loses type info