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Using OCI Generative AI ServiceeasyMultiple ChoiceObjective-mapped

1Z0-1127-25 Using OCI Generative AI Service Practice Question

A team is using OCI Generative AI Agents to build a customer support bot. The bot sometimes generates answers that contradict the knowledge base. What is the most likely cause?

⚠ Common exam trap

Oracle often tests the misconception that hallucinations are always caused by temperature settings, when in fact retrieval quality issues like poor chunking are a more common root cause in RAG-based systems.

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 chunking strategy for the knowledge base does not capture enough context overlap.

When the chunking strategy lacks sufficient context overlap, the retrieved chunks may omit critical surrounding information, causing the generative AI model to infer missing details incorrectly and produce answers that contradict the knowledge base. In OCI Generative AI Agents, the chunking strategy determines how documents are split into smaller pieces for retrieval; without adequate overlap, the model loses the semantic continuity needed to stay faithful to the source material.

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 chunking strategy for the knowledge base does not capture enough context overlap.

    Why this is correct

    If chunks are too small or lack overlap, the model may not retrieve all relevant information, leading to inconsistencies.

  • The max tokens value is too low, truncating the response.

    Why it's wrong here

    Truncation may produce incomplete but not contradictory answers.

  • The temperature parameter is set too high, causing the model to hallucinate.

    Why it's wrong here

    High temperature increases randomness but does not necessarily cause contradictions with retrieved facts.

  • The model's repetition penalty is too high.

    Why it's wrong here

    Repetition penalty discourages repeating tokens, not contradictions.

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