1Z0-1127-25 Fundamentals of Large Language Models Practice Question
An architect is designing a multi-tenant application using OCI Generative AI. Each tenant has custom instructions and data. To minimize cost while maintaining isolation, which deployment approach is recommended?
⚠ Common exam trap
Many exam-takers assume fine-tuning is necessary for customization, overlooking that system prompts and retrieval can achieve equivalent isolation at a fraction of the cost, which the Oracle OCI GenAI exam tests by contrasting dedicated endpoints against shared-model strategies.
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
✓
Shared base model with per-tenant system prompts and retrieval.
It leverages a shared base model with per-tenant system prompts and retrieval-augmented generation (RAG) to isolate custom instructions and data without the cost of dedicated endpoints. This approach minimizes compute overhead by reusing a single model instance while maintaining logical isolation through prompt engineering and vector-based retrieval, aligning with OCI's pay-as-you-go pricing model.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Dedicated fine-tuned endpoint per tenant.
Why it's wrong here
Dedicated endpoints are costly and do not leverage shared infrastructure.
- ✓
Shared base model with per-tenant system prompts and retrieval.
Why this is correct
This approach uses a shared model with tenant-specific prompts and RAG, balancing cost and isolation.
- ✗
On-premises deployment of open-source models.
Why it's wrong here
On-premises deployment shifts operational burden and may not provide the same scalability or integration with OCI.
- ✗
Single large fine-tuned model with conditional logic.
Why it's wrong here
A single model with conditional logic is prone to prompt injection and lacks proper data isolation.
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