Cloud Quest validation error – guard_chat_function not detected

This is a common issue in AWS Cloud Quest lab environments where the validation system doesn't recognize your completed work despite following all instructions correctly.

For your specific issue with the "Moderate - Generative AI Chat App Conversations" lab where the validator can't find your guard_chat_function Lambda function, here are some troubleshooting steps:

1. Double-check that you've followed the lab instructions exactly, including:

• Using the exact function name "guard_chat_function" with correct capitalization and no extra spaces
• Ensuring you're in the us-east-1 region
• Using Python 3.11 runtime
• Assigning the LabRole IAM role

2. Try these additional troubleshooting steps:

• Restart the lab completely (not just repeating the steps)
• Try using a different browser (some users report success switching from Edge to Chrome)
• Clear your browser cache completely
• Disable any browser extensions that might interfere with the AWS console

3. If the issue persists:

• Wait a few minutes after creating the function before attempting validation (sometimes there's a delay in resource recognition)
• Try logging out and back in to refresh your session

This appears to be a technical issue with the lab environment rather than something you're doing wrong. Cloud Quest labs sometimes have validation issues where they don't properly detect correctly configured resources. In some cases, users report that after multiple attempts with no changes to their approach, the lab suddenly validates successfully.
Sources
Can someone help me solve this issue I am having with Lab3 introduction to Amazon EC2 | AWS re:Post
Cloud Quest Generative AI Practitioner - Stuck on "Create an Enterprise knowledge Assistant" | AWS re:Post
Lab 2 is giving me issues | AWS re:Post

This usually pops up in the Generative AI Cloud Quest mission where the validator looks for a Lambda named guard_chat_function. It can’t find it (or finds it in the wrong place), so validation fails. Do these quick checks:

1. Exact name (case-sensitive)

In Lambda console, the function must be exactly guard_chat_function (no suffix/prefix, no capitals).

If you used a different name, rename (create a new one with the right name) and redeploy the same code.

2. Right region & right account

Open the Lambda console in the mission’s region (often the one the lab told you to use).

If you built in another region/account, the validator won’t see it. Recreate or copy it into the correct region/account.

3. Runtime & handler are valid

Lambda shows Last deployment succeeded (no red error banner).

The handler matches your file (e.g., app.lambda_handler if your file is app.py and you exported lambda_handler).

4. Permissions to call Bedrock/Guardrails

The Lambda’s execution role needs permissions to apply a Guardrail and invoke the model (Bedrock).

If your test invocation throws AccessDeniedException, attach/add the missing Bedrock permissions to the role, then test again.

5. API/Trigger optional sanity check

If the scenario also sets up API Gateway, make sure the API integrates with this function (but note: the validator often only checks that the function exists).

Quick ways to verify

Console: Lambda → search for guard_chat_function → confirm region at top right. CLI (if you used it):

aws lambda list-functions --query "Functions[?FunctionName=='guard_chat_function'].FunctionArn"

If this returns nothing, you’re in the wrong region/account or the name doesn’t match.

If it still won’t validate

Re-create a minimal function named guard_chat_function (even a “Hello from Lambda” stub) in the correct region. Many validators only check existence + basic health.