1Z0-1127-25 Fundamentals of Large Language Models Practice Question
An AI engineer is testing a large language model on OCI Generative AI and receives this error: 'Token limit exceeded. Maximum context length is 4096 tokens.' The prompt is 4000 tokens long. What is the most effective way to resolve the issue without losing important context?
⚠ Common exam trap
Oracle often tests the misconception that increasing max_tokens or switching models can bypass the token limit, but the core issue is the total context length, which is a fixed architectural constraint of the model.
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
✓
Reduce the prompt length by summarizing or trimming less relevant information.
The error indicates that the combined prompt and generated output exceed the model's maximum context length of 4096 tokens. Since the prompt alone is 4000 tokens, there is very little room for the model to generate a response. Trimming or summarizing less relevant parts of the prompt directly reduces the token count, allowing the model to produce a complete output without exceeding the limit. This approach preserves the most critical context while staying within the model's 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.
- ✓
Reduce the prompt length by summarizing or trimming less relevant information.
Why this is correct
Reducing prompt length ensures it fits within the token limit while preserving key context.
- ✗
Switch to a model with a larger context window, if available.
Why it's wrong here
While possible, it's not always available; prompt reduction is a more immediate solution.
- ✗
Increase the max_tokens parameter in the API call.
Why it's wrong here
max_tokens controls output length, not input context length.
- ✗
Split the prompt into multiple requests and combine outputs.
Why it's wrong here
Splitting loses cross-references and may produce incoherent results.
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