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Fundamentals of Large Language ModelshardMultiple ChoiceObjective-mapped

1Z0-1127-25 Fundamentals of Large Language Models Practice Question

An architect is optimizing an LLM application that processes long documents. The model has a 4096 token limit, but the documents are often 8000 tokens. They are using a chunking strategy. However, model responses sometimes miss key information that spans across chunks. Which technique most directly addresses this issue?

⚠ Common exam trap

Oracle often tests the misconception that increasing output length (max_tokens) can compensate for input context limitations, but the trap here is that max_tokens only affects the response length, not the model's ability to see the full document.

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

Use overlapping chunks to maintain context continuity.

Overlapping chunks ensure that tokens at the boundaries of one chunk are also present at the start of the next, preserving context continuity. This prevents the model from losing information that spans across chunk boundaries, which is a common issue when processing documents longer than the model's 4096-token context window.

Answer analysis

Option-by-option breakdown

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

  • Randomly select parts of the document to include.

    Why it's wrong here

    Random selection would likely miss key information.

  • Increase the max_tokens parameter for longer outputs.

    Why it's wrong here

    max_tokens affects output length, not input context integration.

  • Use overlapping chunks to maintain context continuity.

    Why this is correct

    Overlapping ensures that information at chunk boundaries is not lost.

  • Use a model with a larger context window.

    Why it's wrong here

    While helpful, it may not be available; overlapping is a direct solution.

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