1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Question
What is the primary benefit of using a Dedicated AI Cluster over On-Demand serving for deploying generative AI models on OCI?
⚠ Common exam trap
Oracle often tests the misconception that Dedicated AI Clusters are cheaper for variable workloads, when in fact their fixed-cost model makes them optimal for steady-state, high-volume inference, not spiky or unpredictable traffic.
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
✓
Higher throughput and lower latency due to reserved capacity
A Dedicated AI Cluster provides reserved compute capacity on OCI, ensuring consistent high throughput and low latency for generative AI inference workloads. Unlike On-Demand serving, which shares resources and can suffer from contention or cold starts, a Dedicated AI Cluster guarantees that GPU resources are always available for your model, eliminating variability in response times.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Higher throughput and lower latency due to reserved capacity
Why this is correct
Reserved capacity minimizes resource contention, improving performance.
- ✗
No need to manage model versions
Why it's wrong here
Model version management is independent of the deployment type.
- ✗
Automatic scaling to zero when not in use
Why it's wrong here
Dedicated AI Clusters run continuously; they do not scale to zero automatically.
- ✗
Lower cost for variable workloads
Why it's wrong here
On-demand is more cost-effective for variable workloads due to pay-per-use.
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