AIF-C01 Applications of Foundation Models Practice Question

Exhibit

Refer to the exhibit.
{
  "completion": "The capital of France is Paris. The capital of Germany is Berlin. The capital of",
  "stop_reason": "max_tokens",
  "usage": {
    "input_tokens": 10,
    "output_tokens": 30
  }
}

Refer to the exhibit. You receive this response from Amazon Bedrock. What is the most likely cause of the incomplete information?

⚠ Common exam trap

AWS often tests the distinction between output truncation (max_tokens) and output quality issues (temperature, prompt engineering), so the trap here is that candidates may incorrectly attribute a truncated response to model ignorance or randomness rather than the explicit token limit.

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 max_tokens limit was reached

The response from Amazon Bedrock shows an incomplete sentence that cuts off mid-thought, which is a classic symptom of hitting the max_tokens limit. When the generated output reaches the specified maximum number of tokens, the model stops generating immediately, resulting in truncated text. This is the most likely cause because the output is syntactically incomplete but otherwise coherent up to the cutoff point.

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 max_tokens limit was reached
Why this is correct
Generation halts once the max_tokens ceiling is hit, so the response ends mid-sentence with content omitted. The truncated, incomplete output in the exhibit matches that hard cutoff, since the model cannot emit further tokens beyond the configured limit.
✗
The prompt was too short
Why it's wrong here
Prompt length alone does not truncate a Bedrock response; short prompts routinely yield complete answers. Brevity is the issue when a prompt omits context, constraints or output-format instructions the model needs, not when the reply itself stops mid-sentence.
✗
The temperature was too high
Why it's wrong here
High temperature increases randomness and creativity, producing varied or rambling text, not truncated or missing information. It is tempting because temperature is the most familiar inference parameter, and lowering it would be correct when the model's output is inconsistent or off-topic rather than incomplete.
✗
The model lacks knowledge about capitals
Why it's wrong here
Bedrock models are pre-trained on vast corpora and do know capital cities, so a knowledge gap would not produce this truncated response. Knowledge limitations matter when asking about obscure, recent or proprietary facts absent from training data, not well-known geography.

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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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