1Z0-1127-25 LLM Fundamentals Practice Question
A company needs to generate embeddings for a large corpus of legal documents to enable semantic search. Which type of model should they use?
⚠ Common exam trap
The 1Z0-1127 exam often tests the misconception that any large language model (LLM) can generate embeddings, but the trap here is that decoder-only models (like GPT) are fundamentally designed for generation, not for producing fixed-size, bidirectional embeddings suitable for semantic search.
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 encoder-only embedding model like Cohere Embed
An encoder-only embedding model like Cohere Embed is designed to convert text into dense vector representations (embeddings) that capture semantic meaning, which is exactly what is needed for semantic search over a large corpus of legal documents. These models use a bidirectional transformer architecture to encode context from both directions, producing fixed-size embeddings that can be efficiently compared using cosine similarity or other distance metrics.
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 encoder-only embedding model like Cohere Embed
Why this is correct
Embedding models are specifically trained to output high-quality embeddings for similarity.
- ✗
A decoder-only generation model like GPT
Why it's wrong here
Generation models produce text, not embeddings optimized for search.
- ✗
A text-to-speech model
Why it's wrong here
Unrelated to text embeddings.
- ✗
A machine translation model
Why it's wrong here
Translation models output text in another language, not embeddings.
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