1Z0-1127-25 LLM Fundamentals Practice Question
A data scientist is building a RAG pipeline on OCI. Which TWO components are essential for the retrieval step?
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
✓
Embedding model to convert chunks into vectors
Chunking splits documents into manageable pieces, and embedding converts them into vectors for similarity search.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Fine-tuned generation model
Why it's wrong here
Fine-tuning is not required for the retrieval step.
- ✓
Embedding model to convert chunks into vectors
Why this is correct
Embeddings are required for vector search.
- ✓
Document chunking
Why this is correct
Chunking is necessary to create retrievable units.
- ✗
Beam search decoder
Why it's wrong here
Beam search is used during text generation, not retrieval.
- ✗
Human feedback loop
Why it's wrong here
Human feedback is not a component of the retrieval step.
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