AI-102 Implement agentic AI solutions Practice Question
You are building an agent for a legal firm that uses Azure OpenAI to analyze contracts. The agent must extract key clauses, identify risks, and summarize the contract. The agent uses a RAG pattern with Azure Cognitive Search as the vector database. After deployment, the agent sometimes returns irrelevant information or fails to find relevant clauses. You suspect the issue is with the chunking strategy. The contracts are large, typically 50-100 pages. Currently, you are chunking by page (each page is one chunk). You want to improve retrieval accuracy. Which action should you take?
⚠ Common exam trap
The trap here is that candidates often focus on tuning parameters like overlap or chunk size, or switching embedding models, without recognizing that the fundamental issue is the chunking strategy's failure to respect the document's logical structure.
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
✓
Change chunking to use semantic boundaries: split at clause or section headings.
Splitting contracts at semantic boundaries (clause or section headings) preserves the natural meaning and context of each chunk, which is critical for legal document analysis. Page-level chunking often splits a clause across two pages, causing the vector search to retrieve incomplete or irrelevant information. By aligning chunks with the document's logical structure, the RAG pattern retrieves more coherent and relevant passages for the Azure OpenAI agent to process.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Keep page-level chunking but add 50% overlap between chunks.
Why it's wrong here
Overlap helps but semantic coherence is more important.
- ✗
Use a different embedding model, such as text-embedding-3-large.
Why it's wrong here
Embedding model is not the root cause.
- ✗
Increase the chunk size to 5 pages per chunk and reduce overlap.
Why it's wrong here
Larger chunks may include irrelevant information.
- ✓
Change chunking to use semantic boundaries: split at clause or section headings.
Why this is correct
Semantic chunking improves relevance.
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