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LangChain and AI Application Development →mediumMultiple ChoiceObjective-mapped

1Z0-1127-25 LangChain and AI Application Development Practice Question

A developer wants to index a large corpus of HTML web pages for a RAG pipeline using LangChain. They need to load the content from URLs, split the text into chunks, and generate embeddings. Which combination of LangChain components should they use?

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

✓

WebBaseLoader, RecursiveCharacterTextSplitter, OCIGenAIEmbeddings

WebBaseLoader loads HTML content, RecursiveCharacterTextSplitter splits it, and OCIGenAIEmbeddings generates embeddings.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • ✗

    PDFLoader, RecursiveCharacterTextSplitter, OCIGenAIEmbeddings

    Why it's wrong here

    PDFLoader is for PDFs, not HTML pages.

  • ✓

    WebBaseLoader, RecursiveCharacterTextSplitter, OCIGenAIEmbeddings

    Why this is correct

    WebBaseLoader loads web pages, splitter splits text, and embeddings generate vectors.

  • ✗

    CSVLoader, RecursiveCharacterTextSplitter, OCIGenAIEmbeddings

    Why it's wrong here

    CSVLoader is for CSV files, not web pages.

  • ✗

    WebBaseLoader, TokenTextSplitter, OCIGenAI

    Why it's wrong here

    OCIGenAI is an LLM wrapper, not an embeddings model.

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