1Z0-1127-25 LLM Fundamentals Practice Question
A practitioner wants to generate embeddings for a set of legal documents to enable semantic search. Which type of model 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
✓
An embedding model like Cohere Embed
Embedding models (e.g., Cohere Embed, OpenAI text-embedding-ada) are specialized to produce dense vector representations. Generation models (like GPT) produce text, not 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.
- ✓
An embedding model like Cohere Embed
Why this is correct
Embedding models output dense vectors that capture semantic meaning, suitable for similarity search.
- ✗
A large language model fine-tuned for classification
Why it's wrong here
Classification models output classes, not embeddings.
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A vision transformer model
Why it's wrong here
Vision transformers are for image data, not text.
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A generative LLM like Cohere Command
Why it's wrong here
Generative models produce text, not embeddings.
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