Content filtering in Prompt Engineering / GenAI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to filter out bad words from the text.

Prompt Engineering / GenAI

bad_words = ['spam', 'scam', 'fake']
text = 'This is a spam message.'
filtered = [word for word in text.split() if [1] not in bad_words]
print(filtered)

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

Aword

Btext

Cbad_words

Dfiltered

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'text' instead of 'word' causes a type error.

Using 'bad_words' inside the condition is incorrect because it's the list, not the current word.

We check each word in the text to see if it is NOT in the list of bad words. So the blank should be 'word'.

2fill in blank

medium

Complete the code to replace bad words with asterisks in the text.

Prompt Engineering / GenAI

bad_words = ['spam', 'scam', 'fake']
text = 'This is a scam message.'
filtered_text = ' '.join([[1] if word in bad_words else word for word in text.split()])
print(filtered_text)

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A'****'

B'####'

C'****', word

D'**** '

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'word' instead of the mask string causes no replacement.

Adding spaces inside the mask string causes formatting issues.

We replace any bad word with '****' to mask it. So the blank should be the string '****'.

3fill in blank

hard

Fix the error in the code to correctly detect if the text contains any bad words.

Prompt Engineering / GenAI

bad_words = ['spam', 'scam', 'fake']
text = 'Beware of fake news.'
contains_bad = any(word [1] bad_words for word in text.split())
print(contains_bad)

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Anot in

Bin

C==

D!=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '==' compares the whole list to a word, which is incorrect.

Using 'not in' reverses the logic and gives wrong results.

To check if any word is in the bad_words list, use 'in'.

4fill in blank

hard

Fill both blanks to create a dictionary counting occurrences of bad words in the text.

Prompt Engineering / GenAI

bad_words = ['spam', 'scam', 'fake']
text = 'spam spam scam fake fake fake'
counts = [1](word for word in text.split() if word [2] bad_words)
print(counts)

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ACounter

Bin

Cnot in

Ddict

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'dict' instead of 'Counter' does not count occurrences automatically.

Using 'not in' reverses the filter and counts good words instead.

We use Counter to count words, and check if word is in bad_words.

5fill in blank

hard

Fill all three blanks to create a function that filters and replaces bad words with a mask.

Prompt Engineering / GenAI

def filter_text(text, bad_words, mask):
    return ' '.join([[1] if word [2] bad_words else word for word in text.split()])

text = 'This is a fake scam message.'
result = filter_text(text, ['spam', 'scam', 'fake'], [3])
print(result)

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

Amask

Bin

C'****'

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'word' instead of 'mask' causes no replacement.

Using 'not in' reverses the logic and replaces good words.

Inside the function, replace words in bad_words with mask. The mask argument is passed as '****'.