- A
The retriever is returning irrelevant chunks due to poor embeddings or low similarity threshold
Irrelevant chunks lead to irrelevant answers; this is a common cause.
- B
The generation model is not conditioned on the retrieved chunks, possibly because the prompt does not instruct it to use them
If the prompt does not tell the model to use the retrieved context, it may ignore it.
- C
The context window of the generation model is smaller than the retrieved chunks
Why wrong: If chunks exceed the context window, they might be truncated, but that would cause loss of detail, not irrelevance.
- D
The temperature is set too low, making outputs deterministic
Why wrong: Low temperature makes outputs less random but does not cause irrelevance to context.
- E
The chunking strategy produces chunks that are too small, losing context
Why wrong: Too-small chunks can miss context but would still be relevant; the issue is irrelevance, not lack of context.
1Z0-1127 LLM Fundamentals Practice Question
This 1Z0-1127 practice question tests your understanding of llm fundamentals. 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 data scientist is debugging a RAG system where the generated answers are not relevant to the retrieved documents. Which TWO factors are MOST likely causing this issue?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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
The retriever is returning irrelevant chunks due to poor embeddings or low similarity threshold
If the retriever returns irrelevant chunks or the generation model ignores the context, the answers will be off-topic. Checking retrieval relevance and prompt instruction is key.
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.
- ✓
The retriever is returning irrelevant chunks due to poor embeddings or low similarity threshold
Why this is correct
Irrelevant chunks lead to irrelevant answers; this is a common cause.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
The generation model is not conditioned on the retrieved chunks, possibly because the prompt does not instruct it to use them
Why this is correct
If the prompt does not tell the model to use the retrieved context, it may ignore it.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
The context window of the generation model is smaller than the retrieved chunks
Why it's wrong here
If chunks exceed the context window, they might be truncated, but that would cause loss of detail, not irrelevance.
- ✗
The temperature is set too low, making outputs deterministic
Why it's wrong here
Low temperature makes outputs less random but does not cause irrelevance to context.
- ✗
The chunking strategy produces chunks that are too small, losing context
Why it's wrong here
Too-small chunks can miss context but would still be relevant; the issue is irrelevance, not lack of context.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Trap categories for this question
Command / output trap
Low temperature makes outputs less random but does not cause irrelevance to context.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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 1Z0-1127 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
- →
LLM Fundamentals — study guide chapter
Learn the concepts, then practise the questions
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FAQ
Questions learners often ask
What does this 1Z0-1127 question test?
LLM Fundamentals — This question tests LLM Fundamentals — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The retriever is returning irrelevant chunks due to poor embeddings or low similarity threshold — If the retriever returns irrelevant chunks or the generation model ignores the context, the answers will be off-topic. Checking retrieval relevance and prompt instruction is key.
What should I do if I get this 1Z0-1127 question wrong?
Identify which 1Z0-1127 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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 →
Last reviewed: Jul 4, 2026
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.
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