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AIF-C01 Applications of Foundation Models Practice Question

Network Topology
$ aws bedrock invoke-modelmodel-id anthropic.claude-v2 \cli-binary-format raw-in-base64-out \Refer to the exhibit.```Assistant:","max_tokens_to_sample":100}' \output.json$ cat output.json

Refer to the exhibit. A user invokes Claude v2 using the AWS CLI. The response is truncated. What is the most likely cause?

⚠ Common exam trap

The AIF-C01 exam often tests the distinction between token limits and stop sequences, where candidates mistakenly attribute truncation to max_tokens_to_sample when the actual cause is a configured stop sequence in the prompt or API parameters.

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 prompt includes a stop sequence 'Assistant:'.

The prompt includes the stop sequence 'Assistant:', which causes the model to halt generation as soon as it encounters that token sequence. In Claude v2, stop sequences are used to control the output length and structure; when the model generates the exact stop sequence, it truncates the response at that point, even if more content could have been produced.

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 AWS CLI is missing the --endpoint-url parameter.

    Why it's wrong here

    The command succeeded; endpoint is not the issue.

  • The max_tokens_to_sample is too low.

    Why it's wrong here

    The stop_reason is stop_sequence, not max_tokens.

  • The model does not support this use case.

    Why it's wrong here

    Claude v2 supports explanation tasks.

  • The prompt includes a stop sequence 'Assistant:'.

    Why this is correct

    Claude uses 'Assistant:' as a stop sequence, causing it to stop generating after its response.

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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

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