1Z0-1127-25 LangChain and AI Application Development Practice Question
A developer is building a RAG pipeline with LangChain. They have loaded PDF documents with PDFLoader. Which TWO steps must they perform before indexing the documents into a vector store?
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
✓
Split the documents into chunks using a text splitter
Documents loaded from PDFs must be split into smaller chunks (using a text splitter) and then embedded into vector representations before they can be indexed in a vector store.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Translate the documents into English
Why it's wrong here
Translation is an optional pre-processing step, not mandatory.
- ✓
Split the documents into chunks using a text splitter
Why this is correct
Splitting ensures each chunk fits the model's token limit and improves retrieval granularity.
- ✗
Encrypt the documents with AES-256
Why it's wrong here
Encryption is a security measure, not a step in the indexing pipeline.
- ✓
Embed the document chunks using an embedding model
Why this is correct
Embedding converts text into vectors that the vector store can index and search.
- ✗
Compress the documents with gzip
Why it's wrong here
Compression is not required for vector indexing.
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