AIF-C01 Applications of Foundation Models Practice Question

Exhibit

Refer to the exhibit.

CloudWatch Log message:
{
  "timestamp": "2025-02-12T10:15:30.000Z",
  "message": "ThrottlingException: Rate exceeded for modelId anthropic.claude-v2. RequestId: abc123",
  "logGroup": "/aws/bedrock/modelinvocations",
  "logStream": "modelinvocations/us-west-2/123456789012"
}

Refer to the exhibit. A developer sees this error when calling Amazon Bedrock for inference. What is the MOST likely cause and recommended solution?

⚠ Common exam trap

Many candidates confuse a throttling error with a model ID or prompt length issue, because the error message may not explicitly state 'throttling' and instead show a generic 'ServiceUnavailable' or 'TooManyRequests' response, leading them to incorrectly modify the model or prompt instead of implementing retry logic.

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

✓

The request rate exceeds the model's throughput limit; implement retries with exponential backoff

The error indicates a throttling exception from Amazon Bedrock, which occurs when the request rate exceeds the model's throughput limit. The recommended solution is to implement retries with exponential backoff to handle transient rate limits gracefully, as this aligns with AWS best practices for managing API call limits.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
The model ID is incorrect; use a different model
Why it's wrong here
A wrong model ID produces a ValidationException naming the identifier, not the throttling or token-limit error shown. Changing models is tempting because model access and identifiers do cause failures, and it would be correct if the error explicitly stated the model could not be found or accessed.
✗
The prompt is too long; reduce the number of tokens in the prompt
Why it's wrong here
Prompt length exceeding the context window returns a specific input-too-long validation error, which the exhibit does not show. Truncating prompts is tempting because oversized inputs genuinely fail, and it would be correct when the error names the maximum input token count being exceeded.
✓
The request rate exceeds the model's throughput limit; implement retries with exponential backoff
Why this is correct
ThrottlingException indicates the account's requests per minute or tokens per minute exceed the model's allocated throughput. Retrying with exponential backoff and jitter spreads retries, letting transient capacity recover instead of compounding the overload with immediate repeat calls.
✗
Increase the max_tokens_to_sample value
Why it's wrong here
max_tokens_to_sample caps generated output length; raising it cannot fix a throttling or input-validation failure. It is tempting because truncated responses are resolved this way, and it would be correct when output stops mid-sentence after hitting the configured token ceiling.

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AIF-C01 Applications of Foundation Models Practice Question

Exhibit

Why each option matters

Option-by-option breakdown

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