1Z0-1127-25 Using OCI Generative AI Service Practice Question
A retail company uses OCI Generative AI Agents to power a product recommendation chatbot on their e-commerce website. The chatbot is integrated with a knowledge base containing product descriptions, customer reviews, and inventory data. Recently, the chatbot has started recommending out-of-stock products frequently, leading to customer frustration. The development team verified that the knowledge base is updated in real-time with inventory data. The chatbot's configuration uses a chunking strategy with a chunk size of 500 tokens and an overlap of 50 tokens. The team suspects the issue is related to how the agent retrieves information. They have access to OCI Logging and Monitoring. Which course of action should the team take first?
⚠ Common exam trap
Oracle often tests the misconception that retrieval issues are always solved by adjusting model parameters (like temperature) or infrastructure scaling, when the real fix lies in tuning the chunking strategy to ensure critical metadata is not lost at chunk boundaries.
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
✓
Increase the chunk overlap from 50 to 150 tokens to ensure inventory status is captured in multiple chunks.
The core issue is that the chatbot retrieves chunks that contain product descriptions but may miss the inventory status because the chunking strategy does not reliably include both pieces of information together. Increasing the chunk overlap from 50 to 150 tokens ensures that inventory data, which may be at the boundary of a chunk, is captured in multiple overlapping chunks, thereby increasing the likelihood that the retrieval step returns a chunk containing both the product and its current stock level. This directly addresses the retrieval gap without altering model behavior or infrastructure.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Decrease the chunk size to 250 tokens to make chunks more specific.
Why it's wrong here
Smaller chunks may lose context and increase the chance of missing inventory status.
- ✗
Reduce the temperature parameter of the model to 0.2 to reduce hallucinations.
Why it's wrong here
The issue is not hallucination but retrieval of inaccurate data; temperature does not affect retrieval.
- ✗
Enable auto-scaling on the AI cluster to improve response speed.
Why it's wrong here
Auto-scaling addresses performance, not the accuracy of recommendations.
- ✓
Increase the chunk overlap from 50 to 150 tokens to ensure inventory status is captured in multiple chunks.
Why this is correct
Greater overlap ensures that inventory updates are not missed, improving the relevance of retrieved context.
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