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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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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

This 1Z0-1127-25 practice question is part of Courseiva's free Oracle certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the 1Z0-1127-25 exam.