1Z0-1127-25 LLM Fundamentals Practice Question
A company has a large dataset of legal documents in multiple languages. They need to find documents semantically similar to a query. Which step is essential for this task?
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
✓
Use a text embedding model to convert documents into dense vector representations
Embedding models convert text into dense vectors that capture semantic meaning. Cosine similarity between query and document embeddings is then used to find similar documents.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Apply BPE tokenization to all documents
Why it's wrong here
Tokenization is a preprocessing step but not sufficient for semantic similarity search.
- ✓
Use a text embedding model to convert documents into dense vector representations
Why this is correct
Embedding models produce vectors that enable semantic similarity computation via cosine similarity.
- ✗
Fine-tune a generation model on the legal documents
Why it's wrong here
Generation models are not designed for semantic similarity search.
- ✗
Use beam search to identify similar passages
Why it's wrong here
Beam search is for sequence generation, not retrieval.
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