The correct choice is to implement a post-generation verification step that checks if the answer is grounded in the retrieved chunks. This technique directly addresses hallucination in RAG by validating each claim in the LLM’s output against the source documents retrieved from OCI OpenSearch, ensuring factual support even when retrieval is accurate. On the Oracle Cloud Infrastructure Generative AI Professional 1Z0-1127 exam, this concept tests your understanding of the RAG pipeline’s vulnerability: retrieval alone does not guarantee faithful generation, so a post-generation grounding check acts as a safety net. A common trap is assuming better retrieval or prompt engineering alone solves hallucination, but the exam emphasizes that verification after generation is the targeted fix. Memory tip: think “verify before you trust”—the grounding check is your final fact-checker against unsupported claims.
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. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 financial firm deploys a RAG application using OCI OpenSearch. They observe that the LLM sometimes generates incorrect answers that are not supported by the retrieved documents. Which technique directly addresses this issue?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Implement a post-generation verification step that checks if the answer is grounded in the retrieved chunks.
Option C is correct because it directly addresses the problem of hallucination by verifying that the LLM's output is factually supported by the retrieved documents. In a RAG pipeline, the LLM may still generate unsupported content even with good retrieval; a post-generation grounding check explicitly validates each claim against the source chunks, ensuring answer fidelity.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 more detailed system prompt instructing the model to not make up information.
Why it's wrong here
Prompts are not a reliable enforcement mechanism.
✗
Increase the temperature parameter of the LLM to reduce creativity.
Why it's wrong here
Higher temperature increases creativity, not reduces it.
✓
Implement a post-generation verification step that checks if the answer is grounded in the retrieved chunks.
Why this is correct
Directly verifies faithfulness.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the number of retrieved documents to provide more context.
Why it's wrong here
May increase hallucinations due to conflicting information.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Oracle often tests the misconception that prompt engineering or parameter tuning alone can solve hallucination in RAG, when in fact a dedicated verification step is required to enforce factual grounding.
Detailed technical explanation
How to think about this question
Post-generation grounding verification typically uses techniques like entailment classification (e.g., using a BERT-based NLI model) to check if each sentence in the answer is entailed by the retrieved chunks. In production, this can be implemented as a separate validation step that rejects or flags answers with low grounding scores, often combined with a fallback mechanism to re-query or abstain. This approach is critical in regulated industries like finance, where unsupported claims can lead to compliance violations.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Building LLM Applications with RAG and Vector Search — This question tests Building LLM Applications with RAG and Vector Search — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Implement a post-generation verification step that checks if the answer is grounded in the retrieved chunks. — Option C is correct because it directly addresses the problem of hallucination by verifying that the LLM's output is factually supported by the retrieved documents. In a RAG pipeline, the LLM may still generate unsupported content even with good retrieval; a post-generation grounding check explicitly validates each claim against the source chunks, ensuring answer fidelity.
What should I do if I get this 1Z0-1127 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
This 1Z0-1127 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 exam.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.