AI-103 Implement Generative AI And Agentic Solutions Practice Question
You are designing a RAG solution using Azure AI Search and Azure OpenAI. Which TWO factors significantly influence the effectiveness of chunking documents during the ingestion phase? (Choose TWO)
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
✓
Chunk size measured in tokens or characters
Chunk size (token length) and chunk overlap are critical parameters that balance retaining context while ensuring chunks fit within embedding model token limits.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Chunk size measured in tokens or characters
Why this is correct
Chunk size determines how much specific detail versus broad context is captured in each vector.
- ✗
Azure OpenAI model temperature setting
Why it's wrong here
Temperature controls generation randomness, which is irrelevant during the document chunking and ingestion phase.
- ✓
Chunk overlap percentage between consecutive segments
Why this is correct
Chunk overlap ensures that sentences crossing boundary lines are not severed from their surrounding context.
- ✗
Azure API Management timeout duration
Why it's wrong here
API timeouts govern HTTP request durations, not document text segmentation.
- ✗
Virtual network subnet IP address range
Why it's wrong here
VNet IP ranges configure network topology and routing, not text chunking strategies.
About these practice questions
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed August 2026 · checked against the official Microsoft exam blueprint
This AI-103 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-103 exam.