1Z0-1127-25 Fundamentals of Large Language Models Practice Question
A user has a prompt that exceeds the model's token limit. What is the best practice to handle this?
⚠ Common exam trap
Oracle often tests the misconception that increasing the max tokens parameter can extend the input capacity, when in reality it only affects output length, not the fixed context window.
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
✓
Summarize the earlier parts of the prompt and include the summary.
When a prompt exceeds the model's token limit, the best practice is to summarize the earlier parts of the prompt and include the summary. This preserves the essential context without exceeding the token limit, as the model's context window is fixed (e.g., 4,096 tokens for GPT-3.5 or 8,192 for GPT-4). Summarization reduces token count while retaining key information, enabling the model to process the entire input within its constraints.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Summarize the earlier parts of the prompt and include the summary.
Why this is correct
Correct: Summarization preserves context while reducing token count.
- ✗
Increase the max tokens parameter in the API call.
Why it's wrong here
Incorrect: Max tokens controls output length, not prompt size.
- ✗
Truncate the prompt and hope the model understands.
Why it's wrong here
Incorrect: Truncation may lose critical context.
- ✗
Split the input into multiple calls and merge results.
Why it's wrong here
Incorrect: Multiple calls are complex and may break coherence.
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