Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions
A large enterprise is evaluating gen AI for internal knowledge management. They need to ensure accuracy and reduce hallucinations. Which strategy is most effective?
⚠ Common exam trap
Google Cloud often tests the misconception that fine-tuning is the universal solution for domain adaptation, but the trap here is that fine-tuning does not provide a dynamic, verifiable knowledge source, whereas RAG explicitly decouples knowledge storage from generation, enabling real-time updates and source attribution.
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)
Retrieval-Augmented Generation (RAG) is the most effective strategy because it grounds the model's responses in an external, authoritative knowledge base, retrieving relevant documents at inference time to provide factual context. This directly reduces hallucinations by ensuring the generated output is based on retrieved evidence rather than relying solely on the model's parametric memory, which is critical for enterprise knowledge management where accuracy is paramount.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Fine-tune a model on domain-specific data
Why it's wrong here
Fine-tuning improves domain knowledge but does not guarantee factual accuracy; hallucinations can persist.
- ✗
Increase model temperature
Why it's wrong here
Higher temperature increases randomness and likelihood of hallucinations.
- ✓
Use Retrieval-Augmented Generation (RAG)
Why this is correct
RAG retrieves relevant documents and conditions the model on them, dramatically reducing hallucinations.
- ✗
Use a larger model without customization
Why it's wrong here
Larger models can still hallucinate and may not be tailored to the 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.