1Z0-1127-25 LangChain and AI Application Development Practice Question
You need to build a RAG pipeline using LangChain and OCI Generative AI. The pipeline must load PDF documents, split them into chunks, embed them, store in a vector store, and retrieve relevant chunks at query time. Which THREE components are essential? (Choose THREE.)
⚠ Common exam trap
The 1Z0-1127 exam often tests the distinction between essential components and optional implementations — candidates mistakenly select Chroma (a specific vector store) as essential, when the core requirement is only that embeddings are stored and retrieved, not that a particular store like Chroma must be used.
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
A is correct because RecursiveCharacterTextSplitter is the standard text splitter in LangChain for splitting documents into chunks while preserving semantic boundaries (e.g., paragraphs, sentences). It recursively splits on different separators (like newlines, spaces) to maintain context, which is critical for effective retrieval in a RAG pipeline.
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
Why this is correct
Text splitter is needed to split large documents into manageable chunks.
- ✓
PDFLoader
Why this is correct
PDFLoader is needed to load PDF documents into LangChain.
- ✓
OCIGenAIEmbeddings
Why this is correct
Embeddings are required to convert text chunks into vectors for storage and retrieval.
- ✗
CSVLoader
Why it's wrong here
CSVLoader loads CSV files, not PDFs; it is not needed here.
- ✗
Chroma
Why it's wrong here
Chroma is a vector store, but not essential; the pipeline could use FAISS or OracleVS instead.
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