Rate limiting and abuse prevention in Prompt Engineering / GenAI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to set a maximum number of requests per minute.

Prompt Engineering / GenAI

rate_limit = [1]  # max requests per minute

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

A100

B1000

C10

D500

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing too high a number that defeats rate limiting purpose.

Setting rate_limit to 100 means the system allows 100 requests per minute.

2fill in blank

medium

Complete the code to check if the user has exceeded the allowed requests.

Prompt Engineering / GenAI

if user_requests > [1]:
    block_user()

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

Arate_limit + 10

Brate_limit

Crate_limit / 2

Drate_limit * 2

Attempts:

3 left

💡 Hint

Common Mistakes

Using a value higher than the limit, which delays blocking.

The user is blocked if their requests exceed the set rate_limit.

3fill in blank

hard

Fix the error in the code that resets the request count after one minute.

Prompt Engineering / GenAI

if time.time() - start_time >= [1]:
    user_requests = 0
    start_time = time.time()

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

A10

B30

C100

D60

Attempts:

3 left

💡 Hint

Common Mistakes

Using 30 or 10 seconds which resets too frequently.

The request count resets every 60 seconds (1 minute) to enforce rate limiting per minute.

4fill in blank

hard

Fill both blanks to implement a simple token bucket rate limiter.

Prompt Engineering / GenAI

bucket_capacity = [1]
tokens = [2]

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

A100

B0

C50

D10

Attempts:

3 left

💡 Hint

Common Mistakes

Setting tokens to zero initially which blocks all requests at start.

The bucket capacity and initial tokens are both set to 100 to allow full capacity at start.

5fill in blank

hard

Fill all three blanks to update tokens and check if a request can proceed.

Prompt Engineering / GenAI

tokens = min([1] + refill_rate, [2])
if tokens >= [3]:
    tokens -= 1
    allow_request()

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

Atokens

Bbucket_capacity

C1

Drefill_rate

Attempts:

3 left

💡 Hint

Common Mistakes

Not capping tokens at bucket capacity or subtracting wrong token amount.

Tokens are refilled but capped at bucket capacity. A request uses 1 token if available.

Practice

1. What is the main purpose of rate limiting in AI services?

easy

A. To improve the accuracy of AI models

B. To increase the speed of AI predictions

C. To stop too many requests from one user in a short time

D. To reduce the size of the AI model

Solution

1. Step 1: Understand rate limiting concept

   Rate limiting is designed to control how many requests a user can make in a short period.

2. Step 2: Identify the main goal

   The goal is to prevent overload and abuse by stopping too many requests quickly.

3. Final Answer:

   To stop too many requests from one user in a short time -> Option C

4. Quick Check:

   Rate limiting = stop excess requests [OK]

Hint: Rate limiting controls request frequency to prevent overload [OK]

Common Mistakes:

• Confusing rate limiting with improving model accuracy
• Thinking rate limiting speeds up predictions
• Assuming rate limiting reduces model size

2. Which Python code snippet correctly implements a simple rate limiter that blocks requests after 5 calls?

easy

A. if requests_count >= 5:
block_request()

B. if requests_count == 5:
allow_request()

C. if requests_count < 5:
block_request()

D. if requests_count > 5:
block_request()

Solution

1. Step 1: Understand the condition for blocking

   We want to block requests when the count reaches or exceeds 5, so >= 5 is correct.

2. Step 2: Check each option

   if requests_count >= 5: block_request() uses '>= 5' to block requests, which matches the requirement.

3. Final Answer:

   if requests_count >= 5: block_request() -> Option A

4. Quick Check:

   Block when count is 5 or more = >= 5 [OK]

Hint: Use '>=' to include the limit value when blocking [OK]

Common Mistakes:

• Using '>' misses blocking exactly at 5
• Using '<' blocks too early
• Allowing request at count 5 instead of blocking

3. Given the code below, what will be printed after 7 calls to check_request()?

requests_count = 0
def block_request():
    print('Blocked')
def allow_request():
    print('Allowed')
def check_request():
    global requests_count
    requests_count += 1
    if requests_count >= 5:
        block_request()
    else:
        allow_request()

for _ in range(7):
    check_request()

medium

A. Allowed printed 7 times

B. Blocked printed 5 times, Allowed printed 2 times

C. Allowed printed 5 times, Blocked printed 2 times

D. Allowed printed 4 times, Blocked printed 3 times

Solution

1. Step 1: Track requests_count and output

   For calls 1 to 4, requests_count is less than 5, so 'Allowed' prints. For calls 5 to 7, requests_count is 5 or more, so 'Blocked' prints.

2. Step 2: Count prints

   'Allowed' prints 4 times, 'Blocked' prints 3 times.

3. Final Answer:

   Allowed printed 4 times, Blocked printed 3 times -> Option D

4. Quick Check:

   4 Allowed + 3 Blocked = 7 calls [OK]

Hint: Count calls before and after limit to find outputs [OK]

Common Mistakes:

• Counting 'Allowed' as 5 times instead of 4
• Confusing when blocking starts
• Ignoring global variable increment

4. The following code is meant to block requests after 2 calls, but it blocks after 3 calls instead. What is the error?

requests_count = 0
def check_request():
    global requests_count
    requests_count += 1
    if requests_count > 3:
        print('Blocked')
    else:
        print('Allowed')

medium

A. The requests_count should start at 1, not 0

B. The condition should be '>= 3' instead of '> 3'

C. The print statements are reversed

D. The global keyword is missing

Solution

1. Step 1: Analyze the blocking condition

   The code blocks only when requests_count > 3, so blocking starts at 4th call, not 3rd.

2. Step 2: Fix condition to block at 3 calls

   Changing condition to '>= 3' will block starting at the 3rd call as intended.

3. Final Answer:

   The condition should be '>= 3' instead of '> 3' -> Option B

4. Quick Check:

   Block at 3 calls means '>= 3' [OK]

Hint: Use '>=' to include the limit call in blocking [OK]

Common Mistakes:

• Using '>' blocks too late
• Starting count at 1 instead of 0 is unnecessary
• Forgetting global keyword (but it's present here)

5. You want to prevent abuse by limiting users to 10 requests per minute. Which approach best combines rate limiting with user tracking in Python?

hard

A. Use a dictionary to store user IDs with timestamps of their requests, then block if more than 10 in last 60 seconds

B. Reset a global request count every minute without user distinction

C. Block all requests after 10 total requests regardless of user

D. Allow unlimited requests but slow down responses after 10 requests

Solution

1. Step 1: Understand per-user rate limiting

   To limit requests per user, we must track each user's request times separately.

2. Step 2: Choose data structure and logic

   A dictionary with user IDs as keys and timestamps as values lets us count requests in the last 60 seconds and block if over 10.

3. Final Answer:

   Use a dictionary to store user IDs with timestamps of their requests, then block if more than 10 in last 60 seconds -> Option A

4. Quick Check:

   Per-user tracking + time window = dictionary with timestamps [OK]

Hint: Track each user's timestamps to count requests per minute [OK]

Common Mistakes:

• Using global count ignores individual users
• Blocking all users after total requests causes unfair blocking
• Slowing responses is not strict rate limiting