Question 217 of 991

Building LLM Applications with RAG and Vector Search →hardMultiple SelectObjective-mapped

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 application for legal document analysis using OCI. Which three best practices should be followed to mitigate hallucinations? (Choose 3.)

Clue words in this question

Noticing these words before you look at the options changes how you read each choice.

Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.

A
Implement a fallback to abstain from answering if confidence is low.
Avoids generating incorrect answers when retrieval is uncertain.
B
Use a low temperature setting for the generation model.
Reduces randomness and likelihood of fabrications.
C
Provide the source document citations in the prompt.
Encourages the model to base answers on provided sources.
D
Include a verification step via a secondary model.
Why wrong: Possible but not standard; can be heavy and introduce latency.
E
Increase the number of retrieved chunks to 10.
Why wrong: More chunks can include noise, potentially increasing hallucinations.

Full breakdown with real-world context →

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 fallback to abstain from answering if confidence is low.

Option A is correct because implementing a fallback to abstain from answering when confidence is low directly reduces hallucinations by preventing the model from generating speculative or incorrect responses. In RAG applications, this is often achieved by setting a confidence threshold on the retrieval scores or the generation model's output probabilities, ensuring the system only responds when it has sufficient evidence.

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.

✓
Implement a fallback to abstain from answering if confidence is low.
Why this is correct
Avoids generating incorrect answers when retrieval is uncertain.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✓
Use a low temperature setting for the generation model.
Why this is correct
Reduces randomness and likelihood of fabrications.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✓
Provide the source document citations in the prompt.
Why this is correct
Encourages the model to base answers on provided sources.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Include a verification step via a secondary model.
Why it's wrong here
Possible but not standard; can be heavy and introduce latency.
✗
Increase the number of retrieved chunks to 10.
Why it's wrong here
More chunks can include noise, potentially increasing hallucinations.

Common exam traps

Common exam trap: answer the scenario, not the keyword

Oracle often tests the misconception that increasing the number of retrieved chunks always improves accuracy, but in reality, it can introduce noise and increase hallucination risk, while a verification step via a secondary model is an advanced technique not listed as a core best practice for mitigating hallucinations in RAG.

Detailed technical explanation

How to think about this question

The temperature setting in generation models controls the randomness of token sampling; a low temperature (e.g., 0.1) makes the model more deterministic and less likely to invent facts, directly reducing hallucinations. Providing source document citations in the prompt grounds the model's output in verifiable evidence, a technique known as 'grounded generation' that forces the model to reference specific chunks rather than relying on its parametric knowledge. Under the hood, RAG systems use embedding similarity scores (e.g., cosine similarity) to retrieve chunks; setting a confidence threshold on these scores ensures that low-relevance chunks are not passed to the generator.

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 practitioner preparing for the 1Z0-1127 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.

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.

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FAQ

Questions learners often ask

What does this 1Z0-1127 question test?

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 fallback to abstain from answering if confidence is low. — Option A is correct because implementing a fallback to abstain from answering when confidence is low directly reduces hallucinations by preventing the model from generating speculative or incorrect responses. In RAG applications, this is often achieved by setting a confidence threshold on the retrieval scores or the generation model's output probabilities, ensuring the system only responds when it has sufficient evidence.

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.

Are there clue words in this question I should notice?

Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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