AI-103 Implement Generative AI And Agentic Solutions Practice Question
You are implementing a RAG pipeline where documents are chunked before embedding generation. You notice that semantic sentences are frequently split across chunk boundaries, resulting in degraded retrieval quality. Which chunking strategy should you implement to resolve this?
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
✓
Sliding window chunking with token overlap
Semantic chunking or sliding window chunking with overlap ensures that context spanning sentence boundaries is preserved across adjacent chunks.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Sliding window chunking with token overlap
Why this is correct
Token overlap ensures adjacent chunks share boundary context, preventing semantic fragmentation.
- ✗
Fixed-character chunking with zero overlap
Why it's wrong here
Zero overlap hard-splits sentences, exacerbating the problem.
- ✗
Paragraph-only splitting with maximum size limits of 10,000 tokens
Why it's wrong here
Oversized chunks degrade embedding specificity and precision.
- ✗
Random token slicing
Why it's wrong here
Random slicing destroys the semantic coherence of the text.
Visual reference
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.