Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions
A research organization is building a generative AI model to assist in drug discovery by generating molecular structures. They have a large dataset of proprietary chemical compounds and want to train a model from scratch. They have extensive ML expertise but limited GPU resources. The organization must comply with strict data privacy regulations that prohibit data from leaving their on-premises environment. Which strategy enables them to train the model efficiently while meeting compliance?
⚠ Common exam trap
Google Cloud often tests the misconception that any cloud GPU instance is sufficient for compliance, but the trap here is that standard GPU instances lack in-use memory encryption, which is required when data privacy regulations prohibit data from leaving the on-premises environment.
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 Google Cloud Confidential VMs with attached GPUs for secure training
Google Cloud Confidential VMs with attached GPUs provide hardware-based memory encryption (using AMD SEV or Intel TDX) that protects data in use, enabling secure training on sensitive proprietary chemical data in the cloud. This allows the organization to leverage scalable GPU resources for efficient model training while maintaining compliance with strict data privacy regulations that prohibit data from leaving their on-premises environment.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Train the model entirely on-premises using existing servers
Why it's wrong here
On-premises servers may lack sufficient GPU capacity.
- ✓
Use Google Cloud Confidential VMs with attached GPUs for secure training
Why this is correct
Confidential VMs encrypt data in use, meeting privacy needs with scalable GPUs.
- ✗
Partner with a cloud provider to train the model on their infrastructure
Why it's wrong here
Outsourcing introduces data exposure risks and may violate compliance.
- ✗
Transfer the data to Google Cloud and use standard GPU instances
Why it's wrong here
Standard instances may not meet strict privacy requirements.
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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.