Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions
A retail company wants to use GenAI to generate product descriptions. They have a small team of data scientists. What is the most efficient approach?
⚠ Common exam trap
Google Cloud often tests the misconception that more data or custom training is always better, but the trap here is that candidates overlook the efficiency and sufficiency of foundation model APIs with prompt engineering for small teams with limited data and compute resources.
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 a foundation model API with prompt engineering and few-shot examples
Using a foundation model API with prompt engineering and few-shot examples is the most efficient approach for a small team. It leverages pre-trained models (e.g., GPT-4, Claude) via API calls, requiring no infrastructure or training data, while prompt engineering and few-shot examples allow the model to adapt to the company's product catalog with minimal effort and cost.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Collect more data for several months before starting
Why it's wrong here
Waiting months for extra data delays delivery without addressing the underlying constraint, since the small team still lacks training capacity; the sessions stall in Allocate before any traffic flows. It is tempting because larger, cleaner datasets genuinely improve fine-tuning quality, and would be right when data volume is the proven bottleneck.
- ✗
Train a model from scratch using their product data
Why it's wrong here
Training from scratch demands massive compute, labelled corpora and ML engineering effort, which a small team cannot sustain; the sessions stall in Allocate before any traffic flows. It is tempting because bespoke training gives full control over tone and domain vocabulary, and would be correct only with abundant data, GPUs and specialist staff.
- ✓
Use a foundation model API with prompt engineering and few-shot examples
Why this is correct
A foundation model API with prompt engineering and few-shot examples avoids training or hosting costs, letting a small data science team deliver results quickly. Fine-tuning or building custom models would demand far more data, compute and specialist effort than the scenario's limited team can sustain.
- ✗
Buy a proprietary model from a startup
Why it's wrong here
Purchasing a proprietary model locks the retailer into a vendor contract and licence costs while offering no adaptation to its catalogue; the sessions stall in Allocate before any traffic flows. It is tempting because buying removes training effort entirely, and would be correct when no suitable pretrained foundation model exists for the domain.
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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.