- A
The vector database has low recall
Why wrong: Low recall would cause missing some relevant documents, but the generated answer might still be based on retrieved documents, not hallucination.
- B
The temperature setting is too high
Why wrong: High temperature increases randomness but does not cause hallucination of facts not in the context; it still conditions on provided text.
- C
The embedding model is not accurately representing document semantics
Why wrong: Poor embeddings could cause retrieval of irrelevant documents, but if retrieved documents are correct, hallucinations are due to other causes.
- D
The LLM's context window is too small to include all retrieved chunks
When the context window truncates retrieved documents, the LLM lacks necessary information and may generate unsupported content.
AI0-001 AI Infrastructure and Technologies Practice Question
This AI0-001 practice question tests your understanding of ai infrastructure and technologies. 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 team uses a retrieval-augmented generation (RAG) system to answer questions from a large enterprise document repository. They observe that the generated answers sometimes contain information not present in the retrieved documents. What is the MOST likely 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's context window is too small to include all retrieved chunks
When the LLM's context window is too small to include all retrieved chunks, the model may generate information not present in the provided context to fill gaps, a phenomenon known as hallucination. This occurs because the model relies on its internal knowledge when relevant retrieved content is truncated, leading to fabricated details. Option D directly addresses this mismatch between retrieval capacity and generation constraints.
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 vector database has low recall
Why it's wrong here
Low recall would cause missing some relevant documents, but the generated answer might still be based on retrieved documents, not hallucination.
- ✗
The temperature setting is too high
Why it's wrong here
High temperature increases randomness but does not cause hallucination of facts not in the context; it still conditions on provided text.
- ✗
The embedding model is not accurately representing document semantics
Why it's wrong here
Poor embeddings could cause retrieval of irrelevant documents, but if retrieved documents are correct, hallucinations are due to other causes.
- ✓
The LLM's context window is too small to include all retrieved chunks
Why this is correct
When the context window truncates retrieved documents, the LLM lacks necessary information and may generate unsupported content.
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between retrieval-side failures (low recall, poor embeddings) and generation-side failures (context window limits, hallucination), trapping candidates who confuse missing information with fabricated information.
Detailed technical explanation
How to think about this question
In RAG systems, the context window size (e.g., 4,096 tokens for GPT-3.5 or 8,192 for GPT-4) limits how many retrieved chunks can be fed to the LLM. When the combined chunk size exceeds this limit, truncation occurs, and the model may resort to parametric knowledge (its training data) to complete the answer, introducing hallucinated content. This is distinct from retrieval failures or generation temperature, as it stems from a capacity bottleneck in the generation phase.
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 AI0-001 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 AI0-001 question test?
AI Infrastructure and Technologies — This question tests AI Infrastructure and Technologies — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The LLM's context window is too small to include all retrieved chunks — When the LLM's context window is too small to include all retrieved chunks, the model may generate information not present in the provided context to fill gaps, a phenomenon known as hallucination. This occurs because the model relies on its internal knowledge when relevant retrieved content is truncated, leading to fabricated details. Option D directly addresses this mismatch between retrieval capacity and generation constraints.
What should I do if I get this AI0-001 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.
About these practice questions
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Last reviewed: Jul 4, 2026
This AI0-001 practice question is part of Courseiva's free CompTIA 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 AI0-001 exam.
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