Error handling and rate limits in Prompt Engineering / GenAI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to catch an error when the API call fails.

Prompt Engineering / GenAI

try:
    response = api_call()
except [1]:
    print("API call failed")

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

AKeyError

BValueError

CTimeoutError

DException

Attempts:

3 left

💡 Hint

Common Mistakes

Using a specific error that may not catch all API failures.

The Exception class catches all errors, which is useful for handling any API call failure.

2fill in blank

medium

Complete the code to check if the API response status code indicates rate limiting.

Prompt Engineering / GenAI

if response.status_code == [1]:
    print("Rate limit exceeded. Please wait.")

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

A404

B429

C200

D500

Attempts:

3 left

💡 Hint

Common Mistakes

Confusing 429 with 404 or 500 status codes.

Status code 429 means too many requests, indicating rate limiting.

3fill in blank

hard

Fix the error in the code to retry the API call after a delay when rate limited.

Prompt Engineering / GenAI

import time

if response.status_code == 429:
    time.sleep([1])
    response = api_call()

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

A0.5

B-1

C5

D"5"

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a string instead of a number to time.sleep.

Using negative sleep time.

The time.sleep() function requires a positive number of seconds as a float or int. Using 5 seconds is a common retry delay.

4fill in blank

hard

Fill both blanks to handle API errors and retry with exponential backoff.

Prompt Engineering / GenAI

max_retries = 3
retry_delay = 1
for attempt in range(max_retries):
    response = api_call()
    if response.status_code == 200:
        break
    elif response.status_code == [1]:
        time.sleep(retry_delay)
        retry_delay [2] 2

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

A429

B*=

C+=

D500

Attempts:

3 left

💡 Hint

Common Mistakes

Using '+=' instead of '*=' for exponential backoff.

Checking wrong status codes.

When rate limited (status 429), the code waits and doubles the delay each retry using *= for exponential backoff.

5fill in blank

hard

Fill all three blanks to log errors, handle rate limits, and raise exceptions after retries.

Prompt Engineering / GenAI

import logging

max_retries = 2
retry_delay = 1
for i in range(max_retries + 1):
    response = api_call()
    if response.status_code == 200:
        break
    elif response.status_code == [1]:
        logging.warning(f"Rate limit hit, retrying in {retry_delay} seconds")
        time.sleep(retry_delay)
        retry_delay [2] 2
    else:
        logging.error(f"API error: {response.status_code}")
        raise [3]("API call failed")

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

A429

B*=

CException

DValueError

Attempts:

3 left

💡 Hint

Common Mistakes

Raising wrong exception type.

Using wrong operator for retry_delay.

Checking incorrect status code.

The code handles rate limit with status 429, doubles retry_delay with '*=', and raises a general Exception on other errors.

Practice

1. What is the main purpose of using error handling in AI applications?

easy

A. To keep the app running smoothly even when problems happen

B. To speed up the AI model training process

C. To increase the number of requests sent to the server

D. To reduce the size of the AI model

Solution

1. Step 1: Understand error handling purpose

   Error handling is used to manage unexpected problems during app execution.

2. Step 2: Connect to AI app context

   In AI apps, error handling helps keep the app running smoothly despite issues.

3. Final Answer:

   To keep the app running smoothly even when problems happen -> Option A

4. Quick Check:

   Error handling = keep app running smoothly [OK]

Hint: Error handling means catching problems to avoid crashes [OK]

Common Mistakes:

• Thinking error handling speeds up training
• Confusing error handling with increasing requests
• Believing error handling reduces model size

2. Which Python syntax correctly catches an error when calling an AI API?

easy

A. try:
response = call_api()
except:
print('Error occurred')

B. catch:
response = call_api()
try:
print('Error occurred')

C. if error:
response = call_api()
else:
print('Error occurred')

D. error handling:
response = call_api()
except:
print('Error occurred')

Solution

1. Step 1: Identify correct try-except syntax

   Python uses try: block followed by except: to catch errors.

2. Step 2: Match syntax with options

   try: response = call_api() except: print('Error occurred') uses correct try-except structure; others use invalid keywords.

3. Final Answer:

   try:\n response = call_api()\nexcept:\n print('Error occurred') -> Option A

4. Quick Check:

   try-except syntax = try: response = call_api() except: print('Error occurred') [OK]

Hint: Remember Python uses try: and except: blocks [OK]

Common Mistakes:

• Using catch instead of except
• Using if error instead of try-except
• Writing invalid keywords like error handling:

3. What will the following Python code print if the API returns a rate limit error?

import time

try:
    response = call_api()
except RateLimitError:
    print('Rate limit hit, waiting...')
    time.sleep(10)
    response = call_api()
print('Done')

medium

A. Error: RateLimitError not caught

B. Done

C. Rate limit hit, waiting...

D. Rate limit hit, waiting...\nDone

Solution

1. Step 1: Understand try-except with RateLimitError

   If call_api() raises RateLimitError, except block runs printing message and waits 10 seconds.

2. Step 2: After waiting, call_api() runs again and then prints 'Done'

   So output includes the message and 'Done' on separate lines.

3. Final Answer:

   Rate limit hit, waiting...\nDone -> Option D

4. Quick Check:

   RateLimitError caught, message + Done printed [OK]

Hint: Exception caught prints message then continues [OK]

Common Mistakes:

• Assuming no message prints
• Thinking program crashes on rate limit
• Ignoring the second call_api() after sleep

4. Identify the error in this code snippet handling rate limits:

try:
    response = call_api()
except RateLimitError
    print('Too many requests')
    time.sleep(5)
    response = call_api()

medium

A. call_api() should not be retried

B. time.sleep() cannot be used in except block

C. Missing colon after except RateLimitError

D. print statement syntax is incorrect

Solution

1. Step 1: Check except syntax

   Python requires a colon ':' after except RateLimitError to start the block.

2. Step 2: Verify other parts

   time.sleep() is valid, retrying call_api() is allowed, print syntax is correct.

3. Final Answer:

   Missing colon after except RateLimitError -> Option C

4. Quick Check:

   except needs colon ':' [OK]

Hint: except lines always end with a colon ':' [OK]

Common Mistakes:

• Forgetting colon after except
• Thinking sleep() is invalid in except
• Believing retry is not allowed

5. You want to build an AI app that calls an API but respects rate limits by retrying after waiting. Which code snippet correctly implements this with error handling and exponential backoff?

hard

A. import time
wait = 1
for _ in range(3):
try:
response = call_api()
break
except RateLimitError:
time.sleep(wait)
wait *= 2

B. import time
wait = 1
while True:
try:
response = call_api()
break
except RateLimitError:
time.sleep(wait)
wait *= 2

C. import time
wait = 1
for _ in range(3):
try:
response = call_api()
except RateLimitError:
wait *= 2
time.sleep(wait)
else:
break

D. import time
wait = 1
while True:
try:
response = call_api()
except RateLimitError:
time.sleep(wait)
wait += 1
else:
break

Solution

1. Step 1: Understand exponential backoff with retries

   We want to retry after waiting, doubling wait time each failure, and stop on success.

2. Step 2: Analyze options for correct loop and break

   import time wait = 1 while True: try: response = call_api() break except RateLimitError: time.sleep(wait) wait *= 2 uses while True loop, tries call_api(), breaks on success, and doubles wait after RateLimitError.

3. Step 3: Check other options

   import time wait = 1 for _ in range(3): try: response = call_api() break except RateLimitError: time.sleep(wait) wait *= 2 breaks on success but uses for loop with fixed tries (less flexible). import time wait = 1 while True: try: response = call_api() except RateLimitError: time.sleep(wait) wait += 1 else: break increments wait linearly, not exponential. import time wait = 1 for _ in range(3): try: response = call_api() except RateLimitError: wait *= 2 time.sleep(wait) else: break doubles wait before sleep, but order is less clear.

4. Final Answer:

   import time wait = 1 while True: try: response = call_api() break except RateLimitError: time.sleep(wait) wait *= 2 -> Option B

5. Quick Check:

   Retry loop with exponential backoff = import time wait = 1 while True: try: response = call_api() break except RateLimitError: time.sleep(wait) wait *= 2 [OK]

Hint: Use while True with break and double wait after error [OK]

Common Mistakes:

• Using for loop limits retries too strictly
• Incrementing wait linearly instead of doubling
• Not breaking loop on success