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1Z0-1127-25 Practice Question: Building LLM Applications with RAG and Vector Search

Which TWO of the following are valid similarity metrics used in vector search?

⚠ Common exam trap

Oracle often tests the distinction between distance metrics (like Euclidean) and similarity metrics (like cosine), and candidates may mistakenly treat all distance-based measures as valid similarity metrics for vector search, overlooking that some are designed for strings or sets rather than continuous vectors.

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

✓

Cosine similarity

Cosine similarity measures the cosine of the angle between two vectors, focusing on orientation rather than magnitude. It is widely used in vector search for comparing embeddings because it effectively captures semantic similarity in high-dimensional spaces, such as those produced by LLMs.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Levenshtein distance
Why it's wrong here
Edit distance for strings.
✓
Cosine similarity
Why this is correct
Commonly used for normalized vectors.
✓
Euclidean distance
Why this is correct
Standard distance metric for vectors.
✗
Hamming distance
Why it's wrong here
Used for binary vectors, not typically for dense embeddings.
✗
Jaccard index
Why it's wrong here
Used for set similarity, not vector similarity.

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1Z0-1127-25 Practice Question: Building LLM Applications with RAG and Vector Search

Why each option matters

Option-by-option breakdown

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