1Z0-1127-25 LangChain and AI Application Development Practice Question
A developer is building a RAG pipeline using LangChain and OCI Generative AI. They need to split a large PDF into overlapping chunks for embedding. Which text splitter and parameter settings are MOST appropriate?
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
✓
RecursiveCharacterTextSplitter with chunk_size=1000, chunk_overlap=200
RecursiveCharacterTextSplitter is designed to split text while maintaining paragraphs and sentences. chunk_size=1000 with chunk_overlap=200 is a typical starting point to preserve context across chunks. TokenTextSplitter counts tokens, which is useful for LLM context limits, but here the requirement is about the text splitter for general use; RecursiveCharacterTextSplitter is the standard choice.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
RecursiveCharacterTextSplitter with chunk_size=1000, chunk_overlap=200
Why this is correct
This splitter attempts to split at natural boundaries (paragraphs, sentences) and the overlap preserves context.
- ✗
CharacterTextSplitter with chunk_size=500, chunk_overlap=50
Why it's wrong here
CharacterTextSplitter splits at character boundaries, often breaking words and losing meaning.
- ✗
TokenTextSplitter with chunk_size=512, chunk_overlap=0
Why it's wrong here
TokenTextSplitter is good for token limits, but zero overlap may lose context between chunks.
- ✗
MarkdownHeaderTextSplitter with chunk_size=1000, chunk_overlap=200
Why it's wrong here
This splitter is specialized for Markdown documents, not generic PDFs.
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