1Z0-1127-25 LLM Fundamentals Practice Question
An OCI user observes that their embedding model returns vectors that are not normalized, and they want to compute cosine similarity between two text embeddings. What should they do?
⚠ Common exam trap
The 1Z0-1127 exam often tests the misconception that the dot product alone is equivalent to cosine similarity, but the trap is that this only holds if the vectors are already normalized to unit length, which is not guaranteed by default.
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 vectors to unit length, then compute the dot product
Cosine similarity measures the cosine of the angle between two vectors, which is equivalent to the dot product of the vectors after they have been normalized to unit length (L2 norm = 1). Option C correctly describes this process: first normalize each embedding vector to unit length, then compute the dot product. This is the standard approach because raw embedding vectors from models like OCI's AI services may not be unit vectors, and the dot product alone does not account for magnitude differences.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Compute the Euclidean distance between the vectors
Why it's wrong here
Euclidean distance is different from cosine similarity.
- ✗
Compute the L1 norm of the difference
Why it's wrong here
L1 norm is not related to cosine similarity.
- ✓
Normalize the vectors to unit length, then compute the dot product
Why this is correct
Cosine similarity is dot product of normalized vectors. Normalizing ensures the result is in [-1,1] and reflects the cosine of the angle.
- ✗
Compute the dot product directly
Why it's wrong here
Dot product without normalization does not equal cosine similarity unless vectors are already unit length.
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