1Z0-1127-25 LLM Fundamentals Practice Question
A team wants to compare the semantic similarity between two sentences using embeddings. Which THREE steps are required?
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
✓
Normalize the resulting vectors to unit length
Generate embeddings for both sentences, then compute cosine similarity. Normalizing vectors ensures cosine similarity equals the dot product. Training a new model is unnecessary, and using a generation model is not appropriate for 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.
- ✗
Use a text generation model to compare the sentences
Why it's wrong here
Generation models are not designed for direct semantic comparison; embedding models are appropriate.
- ✓
Normalize the resulting vectors to unit length
Why this is correct
Normalization ensures that cosine similarity is equivalent to the dot product of the normalized vectors.
- ✓
Compute the cosine similarity between the two vectors
Why this is correct
Cosine similarity measures the angle between the vectors, indicating semantic similarity.
- ✗
Train a new neural network on the two sentences
Why it's wrong here
No training is needed; pretrained embedding models are used directly.
- ✓
Pass both sentences through an embedding model to obtain dense vector representations
Why this is correct
Embedding models produce dense vectors that capture semantic meaning.
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