- A
Reinforcement learning from human feedback (RLHF)
Why wrong: RLHF aligns model behavior but does not inject new knowledge.
- B
Fine-tuning the model on internal documents
Why wrong: Fine-tuning requires retraining and may be costly and not dynamic.
- C
Model distillation to a smaller model
Why wrong: Distillation reduces model size but does not incorporate new documents.
- D
Prompt engineering with retrieval-augmented generation (RAG)
RAG retrieves relevant documents at inference time, providing up-to-date answers.
Question 438 of 1,000
AIF-C01 Fundamentals of Generative AI Practice Question
This AIF-C01 practice question tests your understanding of fundamentals of generative ai. 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 building a chatbot that must provide accurate answers based on internal documents without retraining the model. Which approach should they use?
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
✓
Prompt engineering with retrieval-augmented generation (RAG)
Option D is correct because retrieval-augmented generation (RAG) allows the chatbot to fetch relevant internal documents at inference time and incorporate them into the prompt, providing accurate, up-to-date answers without retraining the model. This approach combines prompt engineering with a retrieval step, ensuring the model's responses are grounded in the company's specific knowledge base while keeping the base model frozen.
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.
- ✗
Reinforcement learning from human feedback (RLHF)
Why it's wrong here
RLHF aligns model behavior but does not inject new knowledge.
- ✗
Fine-tuning the model on internal documents
Why it's wrong here
Fine-tuning requires retraining and may be costly and not dynamic.
- ✗
Model distillation to a smaller model
Why it's wrong here
Distillation reduces model size but does not incorporate new documents.
- ✓
Prompt engineering with retrieval-augmented generation (RAG)
Why this is correct
RAG retrieves relevant documents at inference time, providing up-to-date answers.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse fine-tuning (which requires retraining) with RAG (which does not), or mistakenly think RLHF or distillation can inject new factual knowledge without retraining, when in fact they address alignment, efficiency, or behavior, not dynamic knowledge retrieval.
Detailed technical explanation
How to think about this question
In a RAG pipeline, the user query is first embedded and used to retrieve relevant document chunks from a vector database (e.g., via cosine similarity search on embeddings from a model like text-embedding-ada-002). The retrieved chunks are then inserted into a prompt template alongside the original query, and the LLM generates an answer conditioned on that context, effectively grounding the output in the retrieved data without any weight updates.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this AIF-C01 question test?
Fundamentals of Generative AI — This question tests Fundamentals of Generative AI — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Prompt engineering with retrieval-augmented generation (RAG) — Option D is correct because retrieval-augmented generation (RAG) allows the chatbot to fetch relevant internal documents at inference time and incorporate them into the prompt, providing accurate, up-to-date answers without retraining the model. This approach combines prompt engineering with a retrieval step, ensuring the model's responses are grounded in the company's specific knowledge base while keeping the base model frozen.
What should I do if I get this AIF-C01 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.
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Last reviewed: Jul 4, 2026
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