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.

Practice

1. What is the main purpose of data integrity checks in software testing?

easy

A. To ensure data remains accurate and unchanged during processing

B. To improve the speed of the software

C. To add new features to the software

D. To change data formats automatically

Solution

1. Step 1: Understand the meaning of data integrity

   Data integrity means data is accurate, consistent, and unchanged during operations.

2. Step 2: Identify the goal of data integrity checks

   These checks confirm that data stays correct and unaltered through software processes.

3. Final Answer:

   To ensure data remains accurate and unchanged during processing -> Option A

4. Quick Check:

   Data integrity = data accuracy and consistency [OK]

Hint: Data integrity means data stays correct and unchanged [OK]

Common Mistakes:

• Confusing data integrity with performance improvements
• Thinking data integrity changes data formats
• Assuming data integrity adds new features

2. Which of the following is the correct way to assert that two data values are equal in a test?

easy

A. assertEqual(expected, actual)

B. assertEquals(expected, actual)

C. assertEqual(actual, expected)

D. assertEqual(actual == expected)

Solution

1. Step 1: Recall the correct syntax for equality assertion

   The common syntax is assertEqual(expected, actual) to compare values.

2. Step 2: Check each option for syntax correctness

   assertEqual(expected, actual) matches the correct order and syntax; others have wrong order or wrong method name.

3. Final Answer:

   assertEqual(expected, actual) -> Option A

4. Quick Check:

   assertEqual(expected, actual) is correct [OK]

Hint: Remember: expected value first, actual second in assertEqual [OK]

Common Mistakes:

• Swapping expected and actual parameters
• Using wrong assertion method names
• Passing a boolean expression instead of two values

3. Given the following test code snippet:

expected_data = {'id': 101, 'name': 'Alice'}
actual_data = {'id': 101, 'name': 'Alice'}
assert expected_data == actual_data, 'Data mismatch!'

What will be the test result?

medium

A. Test fails with 'Data mismatch!' error

B. SyntaxError due to wrong assertion

C. Test passes with no error

D. Test passes but prints 'Data mismatch!'

Solution

1. Step 1: Compare expected and actual data dictionaries

   Both dictionaries have the same keys and values, so they are equal.

2. Step 2: Understand assertion behavior

   Since the condition is true, the assertion passes without raising an error.

3. Final Answer:

   Test passes with no error -> Option C

4. Quick Check:

   Equal data means assertion passes [OK]

Hint: Assert passes if expected equals actual data [OK]

Common Mistakes:

• Assuming assertion prints message on success
• Confusing assertion syntax with function calls
• Thinking assertion fails when data matches

4. Identify the error in this data integrity check code:

expected = {'user': 'bob', 'age': 30}
actual = {'user': 'bob', 'age': '30'}
assert expected == actual, 'Mismatch found!'

medium

A. No error, the test will pass

B. The age values have different data types causing assertion failure

C. The dictionary keys are different

D. The assertion syntax is incorrect

Solution

1. Step 1: Compare dictionary keys and values

   Keys are the same, but 'age' values differ: integer 30 vs string '30'.

2. Step 2: Understand assertion behavior with type differences

   Since values differ in type, the assertion fails and raises 'Mismatch found!'.

3. Final Answer:

   The age values have different data types causing assertion failure -> Option B

4. Quick Check:

   Different types cause assertion failure [OK]

Hint: Check data types carefully when comparing values [OK]

Common Mistakes:

• Ignoring type differences in values
• Assuming assertion syntax is wrong
• Thinking keys differ when they don't

5. You have a list of user records with possible duplicate IDs:

users = [
  {'id': 1, 'name': 'Anna'},
  {'id': 2, 'name': 'Ben'},
  {'id': 1, 'name': 'Anna'},
  {'id': 3, 'name': 'Cara'}
]

Which data integrity check best detects duplicate user IDs?

hard

A. Verify all user IDs are integers

B. Compare user names directly for duplicates

C. Sort users by name and check order

D. Check if length of user list equals length of set of user IDs

Solution

1. Step 1: Understand the problem of duplicate IDs

   Duplicates mean some IDs appear more than once in the list.

2. Step 2: Use set to find unique IDs and compare lengths

   Convert IDs to a set (unique values). If set length is less than list length, duplicates exist.

3. Final Answer:

   Check if length of user list equals length of set of user IDs -> Option D

4. Quick Check:

   Set length < list length means duplicates [OK]

Hint: Use set to find unique IDs and compare lengths [OK]

Common Mistakes:

• Checking names instead of IDs for duplicates
• Sorting without checking duplicates
• Only verifying data types, not duplicates