1Z0-1127-25 Faithfulness verification Practice Question
A company is deploying a RAG pipeline using OCI Data Science and OCI Generative AI. The pipeline uses a Cohere command model for generation and a Cohere embed model for retrieval. The team notices that the model occasionally produces hallucinated answers that are not supported by the retrieved context. Which strategy is MOST effective at reducing hallucinations?
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
✓
Implement a faithfulness verification step that re-ranks retrieved passages based on alignment with the generated answer.
Implementing a faithfulness verification step that re-ranks retrieved passages based on alignment with the generated answer directly reduces hallucinations by ensuring the generated output is supported by the retrieved context. Option B is wrong because increasing temperature increases randomness and may lead to more hallucinations. Option C is wrong because increasing the number of retrieved chunks (k) can introduce irrelevant or conflicting information, potentially increasing hallucinations. Option D is wrong because using a larger generative model does not inherently improve faithfulness to the retrieved context and adds computational cost.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Implement a faithfulness verification step that re-ranks retrieved passages based on alignment with the generated answer.
Why this is correct
Correct. Faithfulness verification re-ranks retrieved passages to align with the generated answer, directly reducing hallucinations by filtering unsupported claims.
- ✗
Increase the temperature parameter of the generation model.
Why it's wrong here
Wrong. Increasing temperature increases randomness in generation, which typically increases the likelihood of hallucinations.
- ✗
Increase the number of retrieved chunks (k) to provide more context.
Why it's wrong here
Wrong. Increasing k may add irrelevant or contradictory context, potentially increasing hallucinations rather than reducing them.
- ✗
Use a larger generative model with more parameters.
Why it's wrong here
Wrong. A larger model is not guaranteed to be more faithful; it may still generate ungrounded content and increases cost.
Go deeper
Related to this question
About these practice questions
One of 768 original 1Z0-1127-25 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
JA
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.