Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions
A company is using a generative AI model for internal report generation. They notice costs are high because each request processes large amounts of text. Which business strategy would most effectively reduce costs while maintaining quality?
⚠ Common exam trap
Google Cloud often tests the misconception that 'bigger is always better' or that caching universally reduces costs, but the trap here is that candidates overlook the unique nature of generative AI outputs and the cost benefits of model specialization over raw scale or caching.
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
✓
Fine-tune a smaller model on a specialized dataset.
Fine-tuning a smaller model on a specialized dataset reduces computational cost per inference because smaller models have fewer parameters and require less memory and processing power. By tailoring the model to the company's specific domain (e.g., internal reports), it can maintain output quality comparable to a larger general-purpose model, directly addressing the cost-per-request issue without sacrificing accuracy.
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 smaller model on a specialized dataset.
Why this is correct
A smaller fine-tuned model can provide sufficient quality at lower cost for specific tasks.
- ✗
Use a more powerful model to reduce retries.
Why it's wrong here
More powerful models are generally more expensive per token.
- ✗
Implement caching for repeated requests.
Why it's wrong here
Caching helps if the same request is made many times, but report generation likely has unique requests.
- ✗
Increase the batch size for online predictions.
Why it's wrong here
Online predictions do not use batching; batch size is for offline batch prediction.
Go deeper
Related to this question
About these practice questions
This Generative AI Leader question is part of Courseiva's 683-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
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.