1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Question
An organization is deploying multiple generative AI models on a shared dedicated AI cluster. They need to isolate resource usage for each model to avoid interference. Which strategy is recommended?
⚠ Common exam trap
Oracle often tests the misconception that logical isolation (e.g., compartment quotas or virtual clusters) is sufficient for performance isolation, when in fact hardware-level separation is required to prevent interference in shared AI clusters.
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 each model on its own dedicated AI cluster
Deploying each model on its own dedicated AI cluster provides complete hardware-level isolation, ensuring that resource usage (e.g., GPU memory, compute cycles) for one model does not interfere with another. In OCI Generative AI, dedicated AI clusters are single-tenant instances, so each model gets exclusive access to its allocated infrastructure, eliminating contention. This is the recommended strategy for strict isolation in shared environments.
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 separate fine-tuning jobs for each model
Why it's wrong here
Incorrect: Fine-tuning jobs are temporary; they don't isolate inference-time resources.
- ✗
Configure multiple virtual clusters within the dedicated AI cluster using compartment quotas
Why it's wrong here
Incorrect: Compartment quotas limit usage but do not provide strict compute isolation.
- ✗
Use OCI Resource Manager to allocate resources
Why it's wrong here
Incorrect: Resource Manager manages infrastructure as code, not runtime isolation.
- ✓
Deploy each model on its own dedicated AI cluster
Why this is correct
Correct: Each cluster has dedicated hardware, ensuring no resource contention.
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