1Z0-1127-25 Using OCI Generative AI Service Practice Question
Which TWO factors are most important when deciding between on-demand and dedicated AI clusters for OCI GenAI?
⚠ Common exam trap
Oracle often tests the misconception that fine-tuning capability or model size are primary differentiators between on-demand and dedicated clusters, when in fact both cluster types support these features, and the real decision hinges on concurrency and latency guarantees.
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
✓
Number of concurrent requests
The number of concurrent requests (D) is critical because dedicated AI clusters provide guaranteed throughput and predictable performance for high-volume workloads, while on-demand clusters may throttle or queue requests under heavy load. Latency requirements (E) are equally important because dedicated clusters offer consistent low-latency inference by avoiding resource contention, whereas on-demand clusters can introduce variable latency due to shared infrastructure. Together, these factors directly determine whether a workload needs the isolation and guaranteed resources of a dedicated cluster or can tolerate the elasticity and potential variability of on-demand provisioning.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Fine-tuning capability
Why it's wrong here
Fine-tuning is available in both options; not a deciding factor.
- ✗
Model size
Why it's wrong here
Model size affects both on-demand and dedicated clusters similarly.
- ✗
Data residency
Why it's wrong here
Both deployment types support data residency; it's not a differentiator.
- ✓
Number of concurrent requests
Why this is correct
Dedicated clusters are better for high concurrency due to reserved capacity.
- ✓
Latency requirements
Why this is correct
Dedicated clusters offer lower and more predictable latency.
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