1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Question
A machine learning engineer evaluates OCI Generative AI for a real-time content generation application. They need to meet a SLAs of 99.9% availability. Which deployment architecture satisfies the requirement with the lowest cost?
⚠ Common exam trap
Many candidates assume multi-region deployment is required for high availability, but OCI's 99.9% SLA can be achieved within a single region using availability domains, making the cross-region option unnecessarily expensive.
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
✓
Two dedicated AI clusters in different availability domains.
Deploying two dedicated AI clusters in different availability domains within a single region provides high availability (HA) to meet the 99.9% SLA while minimizing cost. OCI's dedicated AI clusters are regional resources, and placing replicas across availability domains protects against domain-level failures without the cross-region data transfer and egress costs incurred by multi-region deployments.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Two dedicated AI clusters in different regions.
Why it's wrong here
Cross-region deployment incurs higher cost and latency, overkill for 99.9% SLA.
- ✓
Two dedicated AI clusters in different availability domains.
Why this is correct
Clusters in different ADs provide resilience against AD failures at moderate cost.
- ✗
Two dedicated AI clusters in the same availability domain.
Why it's wrong here
Same AD shares physical infrastructure; an AD failure would affect both.
- ✗
Single dedicated AI cluster with a single replica.
Why it's wrong here
A single replica is a single point of failure and cannot meet 99.9% SLA.
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