AI-102 Implement generative AI solutions Practice Question
Your company is building a multi-tenant SaaS application using Azure AI Foundry. Each tenant (customer) should have isolated model deployments and data, but you want to share the base models across tenants to reduce costs. Which approach should you use?
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
✓
Deploy one model per base model and use separate endpoints with routing logic per tenant.
Deploying one model per base model and using separate endpoints with routing logic per tenant provides tenant isolation while sharing the base model, reducing costs. Option A is incorrect because using a single deployment with a tenant ID in the header does not provide sufficient isolation and may lead to data leakage. Option B is incorrect because creating separate fine-tuned models per tenant increases costs and does not share the base model. Option C is incorrect because deploying separate base models for each tenant also increases costs unnecessarily.
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 a single model deployment and a shared endpoint with tenant ID in the header.
Why it's wrong here
Shared endpoint may risk data leakage between tenants.
- ✗
Create separate fine-tuned models for each tenant.
Why it's wrong here
Fine-tuning per tenant increases cost and complexity.
- ✗
Deploy separate base models for each tenant.
Why it's wrong here
Separate deployments increase cost.
- ✓
Deploy one model per base model and use separate endpoints with routing logic per tenant.
Why this is correct
Separate endpoints with routing isolate tenants while sharing the model.
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Written by Johnson Ajibi, MSc IT Security
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This AI-102 practice question is part of Courseiva's free Microsoft 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 AI-102 exam.