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")

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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"

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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"

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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.