The answer is to implement a faithfulness verification step that re-ranks retrieved passages based on alignment with the generated answer. This strategy directly addresses hallucination reduction by performing a faithfulness check after generation, scoring how well each retrieved chunk supports the model’s output, and discarding passages that introduce unsupported claims. On the Oracle Cloud Infrastructure Generative AI Professional 1Z0-1127 exam, this concept tests your understanding of retrieval-augmented generation quality control, where simply increasing temperature, adding more chunks, or using a larger model can worsen hallucinations by introducing randomness or conflicting context. A common trap is assuming more data or a bigger model automatically improves accuracy, but the key is a dedicated verification loop that re-ranks for factual alignment. Memory tip: think “verify then re-rank” to remember that faithfulness checks filter out unsupported content before final output.
1Z0-1127 Practice Question: Building LLM Applications with RAG and Vector Search
This 1Z0-1127 practice question tests your understanding of building llm applications with rag and vector search. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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 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.
Option D is correct because incorporating a faithfulness check that re-ranks retrieval results can directly filter out unsupported claims. Option A is wrong because increasing temperature may increase randomness and hallucinations. Option B is wrong because more retrieved chunks can introduce conflicting information. Option C is wrong because a larger model does not guarantee faithfulness and increases cost.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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
A verification step can detect and mitigate unsupported claims.
Related concept
Static NAT maps one inside address to one outside address.
✗
Increase the temperature parameter of the generation model.
Why it's wrong here
Higher temperature increases randomness, potentially worsening hallucinations.
✗
Increase the number of retrieved chunks (k) to provide more context.
Why it's wrong here
More context can include irrelevant or contradictory information.
✗
Use a larger generative model with more parameters.
Why it's wrong here
Larger models may still hallucinate; size alone does not guarantee faithful output.
Common exam traps
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Trap categories for this question
Command / output trap
Larger models may still hallucinate; size alone does not guarantee faithful output.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
Static NAT maps one inside address to one outside address.
PAT allows many inside hosts to share one public address using ports.
Inside local and inside global describe the private and translated addresses.
NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
→Identify inside and outside interfaces first.
→Check whether the scenario needs static NAT, dynamic NAT or PAT.
→Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Real-world example
How this comes up in practice
A small business has 20 workstations on the 192.168.1.0/24 network and one public IP from its ISP. The router uses PAT (NAT overload) so all 20 devices share one public address using different source ports. NAT questions test whether you understand the four address terms and which direction each translation applies.
What to study next
Got this wrong? Here's your next step.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related 1Z0-1127 NAT questions on configuration and troubleshooting.
Building LLM Applications with RAG and Vector Search — This question tests Building LLM Applications with RAG and Vector Search — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Implement a faithfulness verification step that re-ranks retrieved passages based on alignment with the generated answer. — Option D is correct because incorporating a faithfulness check that re-ranks retrieval results can directly filter out unsupported claims. Option A is wrong because increasing temperature may increase randomness and hallucinations. Option B is wrong because more retrieved chunks can introduce conflicting information. Option C is wrong because a larger model does not guarantee faithfulness and increases cost.
What should I do if I get this 1Z0-1127 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related 1Z0-1127 NAT questions on configuration and troubleshooting.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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Question Discussion
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