1Z0-1127-25 Practice Question: Building LLM Applications with RAG and Vector Search
A developer notices that the RAG application returns irrelevant chunks for user queries. The embedding model used is `cohere.embed-english-light-v3.0`. Which action is MOST likely to improve relevance?
⚠ Common exam trap
Oracle often tests the misconception that tuning retrieval parameters (k, chunk size, similarity metric) can compensate for a weak embedding model, when in fact the embedding quality is the foundational factor for relevance in RAG systems.
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
✓
Switch to a larger embedding model (e.g., cohere.embed-english-v3.0)
The `cohere.embed-english-light-v3.0` model is a smaller, faster embedding model that may lack the semantic richness needed to capture nuanced query-document relationships. Switching to the larger `cohere.embed-english-v3.0` model provides higher-dimensional embeddings with better representational capacity, which directly improves the relevance of retrieved chunks in a RAG pipeline.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reduce the number of retrieved chunks (k)
Why it's wrong here
Reducing k may exclude relevant chunks, worsening coverage.
- ✗
Increase the chunk size
Why it's wrong here
Larger chunks may dilute specific information and reduce precision.
- ✓
Switch to a larger embedding model (e.g., cohere.embed-english-v3.0)
Why this is correct
Larger models produce higher-quality embeddings, improving retrieval relevance.
- ✗
Use a different similarity metric (e.g., Euclidean instead of cosine)
Why it's wrong here
Changing similarity metric has minor impact compared to model quality.
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