Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions
A retail company with a large FAQ database wants to build a generative AI customer service chatbot that can answer questions accurately with up-to-date information. Which business strategy should they prioritize?
⚠ Common exam trap
Google Cloud often tests the misconception that fine-tuning is the best way to inject domain knowledge, but the trap here is that fine-tuning cannot efficiently handle frequently changing data, whereas RAG provides a modular, update-friendly architecture that avoids retraining costs.
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 vector search on the FAQ database.
Retrieval-augmented generation (RAG) with vector search allows the chatbot to dynamically retrieve the most relevant, up-to-date FAQ entries from a large database at inference time, grounding the generative model's responses in verified content without requiring retraining. This approach combines the flexibility of a pre-trained language model with the accuracy of real-time information retrieval, ensuring answers reflect the latest FAQ updates.
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 vector search on the FAQ database.
Why this is correct
RAG retrieves current, relevant information from the database, providing accurate and fresh responses without model retraining.
- ✗
Train a new model from scratch using the FAQ data.
Why it's wrong here
Training from scratch is expensive, time-consuming, and still requires retraining for updates.
- ✗
Fine-tune a foundational model on the entire FAQ dataset.
Why it's wrong here
Fine-tuning would require frequent retraining to keep up with updates and may still fail on out-of-distribution queries.
- ✗
Use a general-purpose language model without any customization.
Why it's wrong here
A general-purpose model may hallucinate or provide generic answers that are not aligned with the company's specific knowledge base.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.