1Z0-1127-25 LLM Fundamentals Practice Question
A machine learning engineer is designing a RAG pipeline in OCI to improve the accuracy of an LLM-based FAQ bot. Which TWO components are essential for the retrieval phase? (Select TWO.)
⚠ Common exam trap
The 1Z0-1127 exam often tests the distinction between retrieval-phase components (chunking and embeddings) and generation-phase components (tokenization and the LLM itself), leading candidates to mistakenly include reranking as essential when it is only an optional refinement.
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
✓
Document chunking
Document chunking is essential because it breaks large documents into smaller, manageable pieces that can be individually indexed and retrieved. Without chunking, the retrieval phase would either miss relevant context or return overly large documents that exceed the LLM's context window, reducing accuracy.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Document chunking
Why this is correct
Documents must be split into chunks for effective retrieval.
- ✗
Tokenization before the generation step
Why it's wrong here
Tokenization is used in generation but is not a retrieval component.
- ✗
Text generation model
Why it's wrong here
Generation is part of the answer synthesis phase, not retrieval.
- ✗
A reranker model
Why it's wrong here
Reranking is an optional enhancement, not a core retrieval component.
- ✓
Embedding model to convert chunks into vectors
Why this is correct
Embeddings are needed for semantic search over chunks.
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