Data integrity checks in Testing Fundamentals - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

What is the primary goal of data integrity checks in software testing?

Choose the best description of what data integrity checks aim to ensure during software testing.

A To check if the user interface displays data in the correct font and color.

B To test the speed of data retrieval from the database under heavy load.

C To ensure that data is encrypted before transmission over the network.

D To verify that data remains accurate, consistent, and unaltered during processing and storage.

💡 Hint

Think about what 'integrity' means in everyday life, like keeping your personal records correct and unchanged.

Data integrity checks focus on making sure data stays correct and consistent throughout its lifecycle, preventing accidental or malicious changes.

❓ Predict Output

intermediate

What is the output of this data validation test code?

Consider this Python code that checks if a list of user IDs contains duplicates. What will it print?

Testing Fundamentals

user_ids = [101, 202, 303, 101, 404]
unique_ids = set(user_ids)
if len(user_ids) == len(unique_ids):
    print('No duplicates found')
else:
    print('Duplicates detected')

A Duplicates detected

B SyntaxError due to missing colon

C No duplicates found

D TypeError because set() cannot be used on list

💡 Hint

Check if the list has repeated values and how sets handle duplicates.

The list contains the value 101 twice, so the set will have fewer items. The code prints 'Duplicates detected'.

❓ assertion

advanced

Which assertion correctly verifies data integrity after a database update?

You updated a user's email in the database. Which assertion best confirms the update was successful and data integrity is maintained?

A assert updated_user.email == '[e-mail]'

B assert updated_user.email != '[e-mail]'

C assert updated_user.email == None

D assert updated_user.email == 12345

💡 Hint

The assertion should confirm the email matches the new value exactly.

Option A checks that the email field equals the new email, confirming the update and data integrity.

🔧 Debug

advanced

Identify the error in this data integrity test code snippet

Find the bug in this Python code that verifies if all records have a non-empty 'name' field.

Testing Fundamentals

records = [{'name': 'Alice'}, {'name': 'Bob'}, {'name': ''}]
for record in records:
    assert record['name'], 'Name field is empty!'
print('All records have names')

A The code raises a KeyError because 'name' key is missing.

B The assertion fails because one record has an empty name string.

C The code has a SyntaxError due to missing colon after for loop.

D The print statement is inside the loop causing multiple prints.

💡 Hint

Look for any record with an empty or missing 'name' value.

The third record has an empty string for 'name', so the assertion fails and raises an AssertionError.

❓ framework

expert

Which test framework feature best supports automated data integrity checks?

When designing automated tests for data integrity, which feature of a test framework is most helpful?

A Built-in UI components for creating graphical test reports.

B Code coverage reports showing which lines of code were executed.

C Fixtures to set up and tear down consistent test data before and after tests run.

D Syntax highlighting to make test code easier to read.

💡 Hint

Think about how to ensure tests start with known good data and clean up after themselves.

Fixtures help prepare a stable environment and data for tests, which is crucial for reliable data integrity checks.