Content filtering in Prompt Engineering / GenAI - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

What is the main purpose of content filtering in AI?

Content filtering is used in AI systems to manage what kind of content is shown or blocked. What is the main goal of content filtering?

A To prevent harmful or inappropriate content from being shown

B To improve the speed of AI model training

C To increase the size of the training dataset

D To reduce the number of model parameters

💡 Hint

Think about why some AI systems block certain words or images.

Content filtering helps AI systems avoid showing harmful, offensive, or inappropriate content to users, ensuring safer and more responsible AI use.

❓ Predict Output

intermediate

What is the output of this content filtering code snippet?

Given the following Python code that filters out banned words from a list of messages, what is the output?

Prompt Engineering / GenAI

banned_words = {'bad', 'ugly'}
messages = ['This is good', 'This is bad', 'Ugly truth']
filtered = [msg for msg in messages if not any(word.lower() in banned_words for word in msg.split())]
print(filtered)

A ['This is bad', 'Ugly truth']

B ['This is good']

C ['This is good', 'Ugly truth']

D ['This is good', 'This is bad']

💡 Hint

Check which messages contain words in the banned_words set.

The code removes messages containing any banned word. 'This is bad' contains 'bad' and 'Ugly truth' contains 'Ugly' (case insensitive), so only 'This is good' remains.

❓ Model Choice

advanced

Which model type is best suited for real-time content filtering in chat applications?

You want to filter harmful content in a chat app instantly as users type. Which AI model type is best for this task?

A Lightweight rule-based model with keyword matching

B Large batch-trained transformer model with high latency

C Heavy deep learning model requiring GPU inference

D Offline unsupervised clustering model

💡 Hint

Consider speed and resource use for real-time filtering.

Lightweight rule-based models with keyword matching are fast and efficient for real-time filtering, unlike large or heavy models that cause delays.

❓ Hyperparameter

advanced

Which hyperparameter adjustment helps reduce false positives in a content filtering model?

You have a content filtering model that blocks too many safe messages (false positives). Which hyperparameter change can help reduce this?

A Lower the classification threshold

B Increase the learning rate

C Increase the classification threshold

D Decrease the batch size

💡 Hint

Think about how threshold affects model sensitivity.

Increasing the classification threshold makes the model more strict before blocking content, reducing false positives but possibly increasing false negatives.

🔧 Debug

expert

Why does this content filtering code fail to block the word 'Bad'?

Examine the code below. It should block messages containing the word 'bad' regardless of case, but it does not block 'Bad'. Why?

Prompt Engineering / GenAI

banned_words = {'bad'}
message = 'This is Bad'
if any(word.lower() in banned_words for word in message.split()):
    print('Blocked')
else:
    print('Allowed')

A The any() function is used incorrectly

B The banned_words set should include 'Bad' with uppercase B

C The split() method does not split the message correctly

D The code does not convert message words to lowercase before checking

💡 Hint

Check how case sensitivity affects membership tests.

The code checks words as-is against banned_words which only has lowercase 'bad'. 'Bad' with uppercase B does not match, so it is not blocked.

Practice

1. What is the main purpose of content filtering in AI systems?

easy

A. To block or clean harmful text to keep users safe

B. To speed up the AI model training process

C. To increase the size of the training dataset

D. To improve the AI model's accuracy on images

Solution

1. Step 1: Understand content filtering purpose

   Content filtering is designed to detect and remove harmful or unsafe text to protect users.

2. Step 2: Compare options to purpose

   Only To block or clean harmful text to keep users safe matches this goal; others relate to unrelated AI tasks.

3. Final Answer:

   To block or clean harmful text to keep users safe -> Option A

4. Quick Check:

   Content filtering = block harmful text [OK]

Hint: Content filtering = blocking harmful or unsafe text [OK]

Common Mistakes:

• Confusing filtering with training speed
• Thinking filtering improves image accuracy
• Assuming filtering increases data size

2. Which of the following is a correct way to check if a text contains a banned word in Python?

easy

A. if text.has(banned_word):

B. if text.contains(banned_word):

C. if banned_word in text:

D. if banned_word inside text:

Solution

1. Step 1: Recall Python syntax for substring check

   In Python, the correct way to check if a substring is in a string is using in.

2. Step 2: Evaluate each option

   if banned_word in text: uses correct syntax; others use invalid or non-Python methods.

3. Final Answer:

   if banned_word in text: -> Option C

4. Quick Check:

   Substring check in Python uses 'in' keyword [OK]

Hint: Use 'in' keyword to check substring in Python strings [OK]

Common Mistakes:

• Using non-existent methods like contains()
• Using wrong keywords like 'inside'
• Confusing syntax from other languages

3. Given the code below, what will be the output?

bad_words = ['spam', 'scam']
text = 'This message contains spam and scam.'
filtered = any(word in text for word in bad_words)
print(filtered)

medium

A. None

B. False

C. Error

D. True

Solution

1. Step 1: Understand the any() function with generator

   The expression checks if any bad word is found in the text. Since 'spam' and 'scam' are both in the text, any() returns True.

2. Step 2: Confirm print output

   Printing filtered will output True because the condition is met.

3. Final Answer:

   True -> Option D

4. Quick Check:

   any() finds bad words = True [OK]

Hint: any() returns True if any bad word is found in text [OK]

Common Mistakes:

• Thinking any() returns False if multiple matches
• Confusing any() with all()
• Expecting an error due to syntax

4. Identify the error in this content filtering code snippet:

bad_words = ['bad', 'ugly']
text = 'This is a bad example.'
if bad_words in text:
    print('Filtered')
else:
    print('Clean')

medium

A. Using 'in' to check list in string is incorrect

B. Missing colon after if statement

C. bad_words should be a string, not a list

D. print statement syntax is wrong

Solution

1. Step 1: Analyze the 'if' condition

   The code tries to check if a list is in a string, which is invalid in Python.

2. Step 2: Correct way to check bad words in text

   We should check each word individually, e.g., using any(word in text for word in bad_words).

3. Final Answer:

   Using 'in' to check list in string is incorrect -> Option A

4. Quick Check:

   Cannot check list in string directly [OK]

Hint: Check each word, not whole list, when filtering text [OK]

Common Mistakes:

• Trying to use 'in' with list and string directly
• Ignoring need for loop or any()
• Assuming list membership works on strings

5. You want to replace all banned words in a user message with '[CENSORED]'. Which code snippet correctly does this for the list banned = ['bad', 'ugly'] and string msg = 'This is a bad and ugly day.'?

hard

A. msg = msg.replace(banned, '[CENSORED]')
print(msg)

B. for word in banned:
msg = msg.replace(word, '[CENSORED]')
print(msg)

C. msg = '[CENSORED]' if word in banned else msg
print(msg)

D. msg = msg.filter(lambda w: w not in banned)
print(msg)

Solution

1. Step 1: Understand string replacement for multiple words

   We must replace each banned word one by one using a loop and str.replace().

2. Step 2: Evaluate each option

   for word in banned: msg = msg.replace(word, '[CENSORED]') print(msg) correctly loops and replaces; B tries to replace list directly (invalid); C uses wrong syntax; D uses filter on string (invalid).

3. Final Answer:

   for word in banned: msg = msg.replace(word, '[CENSORED]') print(msg) -> Option B

4. Quick Check:

   Loop and replace each banned word [OK]

Hint: Replace banned words one by one with a loop and replace() [OK]

Common Mistakes:

• Trying to replace list directly in string
• Using filter on string instead of list
• Incorrect conditional replacement syntax