Fundamentals of Large Language Models →hardMultiple ChoiceObjective-mapped

1Z0-1127-25 Fundamentals of Large Language Models Practice Question

A company uses RAG (Retrieval-Augmented Generation) with OCI OpenSearch and OCI Generative AI. The system retrieves irrelevant documents. What is the first step to debug?

⚠ Common exam trap

Oracle OCI GenAI exams often test the misconception that retrieval issues are caused by the LLM or its parameters, when in fact the root cause is almost always in the embedding or indexing pipeline.

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

✓

Check the embeddings quality

When RAG retrieves irrelevant documents, the most common root cause is poor embedding quality—vectors that fail to capture semantic similarity between the query and the documents. Checking embeddings (e.g., cosine similarity scores, dimensionality, or model used) is the logical first step before adjusting retrieval parameters or the LLM itself.

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 different LLM
Why it's wrong here
LLM affects generation, not retrieval relevance.
✗
Increase the number of retrieved documents
Why it's wrong here
More documents can introduce more irrelevant content; better embeddings is the root cause.
✓
Check the embeddings quality
Why this is correct
Embeddings directly impact retrieval relevance; low-quality embeddings cause irrelevant results.
✗
Lower the temperature
Why it's wrong here
Temperature affects output randomness, not retrieval.

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1Z0-1127-25 Fundamentals of Large Language Models Practice Question

Why each option matters

Option-by-option breakdown

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