- A
The chunk_size in the text splitter is too large
Why wrong: Chunk size affects retrieval granularity but does not directly cause the LLM to ignore retrieved context.
- B
The embedding model is not compatible with the retriever
Why wrong: Embedding compatibility affects retrieval quality but not the generation step; the LLM could still ignore good retrieved context.
- C
The LLM prompt does not instruct the model to base its answer solely on the provided context
The prompt should explicitly constrain the LLM to answer only from the retrieved documents; otherwise, the LLM may use its internal knowledge, leading to ungrounded answers.
- D
The retriever is returning irrelevant documents
Why wrong: If retrieval were irrelevant, the answers would likely be unrelated but still based on what was retrieved; ungrounded answers suggest the LLM is not using the retrieved context.
1Z0-1127 LangChain and AI Application Development Practice Question
This 1Z0-1127 practice question tests your understanding of langchain and ai application development. 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.
An application uses ConversationalRetrievalChain with a vector store retriever. Users report that the chatbot sometimes provides answers that are not grounded in the retrieved documents. Which step in the RAG pipeline is most likely the cause?
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 LLM prompt does not instruct the model to base its answer solely on the provided context
Option C is correct because the ConversationalRetrievalChain in LangChain relies on the LLM prompt to instruct the model to base its answer solely on the provided context. If the prompt does not include such an instruction, the LLM may generate answers using its pre-trained knowledge rather than the retrieved documents, leading to ungrounded responses. This is a common oversight in RAG pipeline design where the prompt template fails to enforce context-only generation.
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 chunk_size in the text splitter is too large
Why it's wrong here
Chunk size affects retrieval granularity but does not directly cause the LLM to ignore retrieved context.
- ✗
The embedding model is not compatible with the retriever
Why it's wrong here
Embedding compatibility affects retrieval quality but not the generation step; the LLM could still ignore good retrieved context.
- ✓
The LLM prompt does not instruct the model to base its answer solely on the provided context
Why this is correct
The prompt should explicitly constrain the LLM to answer only from the retrieved documents; otherwise, the LLM may use its internal knowledge, leading to ungrounded answers.
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 retriever is returning irrelevant documents
Why it's wrong here
If retrieval were irrelevant, the answers would likely be unrelated but still based on what was retrieved; ungrounded answers suggest the LLM is not using the retrieved context.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that retrieval quality (chunk size, embeddings, or document relevance) is the primary cause of ungrounded answers, when in fact the prompt instruction to the LLM is the critical control point in the RAG pipeline.
Detailed technical explanation
How to think about this question
Under the hood, LangChain's ConversationalRetrievalChain uses a default prompt template that includes a system message like 'Use the following pieces of context to answer the question at the end.' If this instruction is omitted or weakened, the LLM may fall back to its parametric knowledge, especially for common topics. In real-world scenarios, this is often seen when the prompt is customized for chat history but the context grounding directive is accidentally removed, causing the model to hallucinate or generate answers from training data.
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?
LangChain and AI Application Development — This question tests LangChain and AI Application Development — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The LLM prompt does not instruct the model to base its answer solely on the provided context — Option C is correct because the ConversationalRetrievalChain in LangChain relies on the LLM prompt to instruct the model to base its answer solely on the provided context. If the prompt does not include such an instruction, the LLM may generate answers using its pre-trained knowledge rather than the retrieved documents, leading to ungrounded responses. This is a common oversight in RAG pipeline design where the prompt template fails to enforce context-only generation.
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: "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.
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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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