- A
Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store
RAG retrieves the latest document chunks at query time, eliminating the need to retrain.
- B
Fine-tune a base LLM on the policy documents monthly
Why wrong: Monthly fine-tuning is costly and time-consuming; knowledge becomes stale quickly.
- C
Use a larger foundation model with a longer context window and paste all documents into each prompt
Why wrong: This is impractical due to context length limits and high token costs.
- D
Train a custom model from scratch on the policy documents each month
Why wrong: Training from scratch is extremely expensive and time-consuming.
Question 530 of 997
Generative AI Leader Applying Generative AI in Business Practice Question
This Generative AI Leader practice question tests your understanding of applying generative ai in business. 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 company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?
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
✓
Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store
Retrieval-Augmented Generation (RAG) is the most appropriate approach because it allows the chatbot to retrieve relevant policy document chunks from a vector store at inference time, without requiring model retraining when documents are updated monthly. This decouples the knowledge base from the model weights, enabling dynamic updates by simply re-indexing the new documents into the vector database.
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.
- ✓
Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store
Why this is correct
RAG retrieves the latest document chunks at query time, eliminating the need to retrain.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Fine-tune a base LLM on the policy documents monthly
Why it's wrong here
Monthly fine-tuning is costly and time-consuming; knowledge becomes stale quickly.
- ✗
Use a larger foundation model with a longer context window and paste all documents into each prompt
Why it's wrong here
This is impractical due to context length limits and high token costs.
- ✗
Train a custom model from scratch on the policy documents each month
Why it's wrong here
Training from scratch is extremely expensive and time-consuming.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that fine-tuning is the only way to incorporate domain knowledge, but here the key trap is ignoring the cost and frequency of updates—candidates may pick fine-tuning (B) because it seems 'customized,' without realizing RAG avoids retraining entirely.
Detailed technical explanation
How to think about this question
Under the hood, RAG uses a retriever (e.g., a dense passage retriever like DPR or a sparse retriever like BM25) to encode documents into embeddings stored in a vector database (e.g., Pinecone, FAISS). At query time, the retriever finds the top-k most similar chunks via cosine similarity, and the LLM generates an answer conditioned on those chunks. A subtle behavior is that chunk overlap and embedding model choice significantly impact retrieval accuracy; for example, using a model fine-tuned on domain-specific data (e.g., sentence-transformers/all-MiniLM-L6-v2) can improve relevance over a generic model.
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.
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 Generative AI Leader question test?
Applying Generative AI in Business — This question tests Applying Generative AI in Business — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store — Retrieval-Augmented Generation (RAG) is the most appropriate approach because it allows the chatbot to retrieve relevant policy document chunks from a vector store at inference time, without requiring model retraining when documents are updated monthly. This decouples the knowledge base from the model weights, enabling dynamic updates by simply re-indexing the new documents into the vector database.
What should I do if I get this Generative AI Leader 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
This Generative AI Leader practice question is part of Courseiva's free Google Cloud 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 Generative AI Leader exam.
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