Data integrity checks in Testing Fundamentals - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to check if the data list is not empty before processing.

Testing Fundamentals

if [1]:
    print("Data is ready for processing")

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

Alen(data) == 0

Bdata == None

Cdata is False

Dlen(data) > 0

Attempts:

3 left

💡 Hint

Common Mistakes

Checking if data is None instead of checking its length.

Using equality to False which is not correct for lists.

We use len(data) > 0 to check if the list has any items, ensuring data is present.

2fill in blank

medium

Complete the code to assert that the 'user_id' field exists in the data dictionary.

Testing Fundamentals

assert [1] in data, "user_id missing"

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

Adata['user_id']

Buser_id

C'user_id'

Ddata.user_id

Attempts:

3 left

💡 Hint

Common Mistakes

Using data['user_id'] directly which causes error if key missing.

Using variable user_id without quotes.

We check if the string key 'user_id' is in the dictionary keys.

3fill in blank

hard

Fix the error in the assertion that checks if the 'age' value is an integer.

Testing Fundamentals

assert isinstance(data.get('age'), [1]), "Age must be integer"

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

Alist

Bint

Cstr

Dfloat

Attempts:

3 left

💡 Hint

Common Mistakes

Using string or float types instead of int.

Not using isinstance and causing runtime errors.

The isinstance function checks if the value is of type int for age.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that keeps only keys with non-empty string values.

Testing Fundamentals

{k: v for k, v in data.items() if v [1] '' and isinstance(v, [2])}

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A!=

B==

Cstr

Dint

Attempts:

3 left

💡 Hint

Common Mistakes

Using '==' instead of '!=' causing wrong filtering.

Checking for int type instead of str.

The comprehension filters keys where value is not empty string and is a string type.

5fill in blank

hard

Fill all three blanks to create a test that asserts all values in the list are unique.

Testing Fundamentals

assert len([1]) == len([2]), "Values are not unique"
unique_values = set([3])

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Avalues

Bset(values)

Attempts:

3 left

💡 Hint

Common Mistakes

Comparing set to list in wrong order.

Using different variable names causing errors.

The test compares the length of the list values with the length of the set of values which removes duplicates.

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