1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Question
A company is deploying a fine-tuned Cohere model on OCI Generative AI service for real-time inference. They need to ensure low latency even during demand spikes. Which configuration should they prioritize?
⚠ Common exam trap
Oracle often tests the misconception that caching or streaming alone can solve latency under load, when in fact only dedicated compute resources guarantee isolation and consistent performance during demand spikes.
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
✓
Use a dedicated AI cluster for the endpoint.
A dedicated AI cluster provides isolated compute resources (GPUs) that are not shared with other tenants or workloads, ensuring consistent low latency even under demand spikes. This is critical for real-time inference because shared endpoints can experience resource contention and throttling during high traffic, while a dedicated cluster guarantees predictable performance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable model caching on the endpoint.
Why it's wrong here
Caching improves response times for repeated queries but does not address latency during spikes.
- ✓
Use a dedicated AI cluster for the endpoint.
Why this is correct
A dedicated AI cluster with autoscaling ensures consistent low latency under variable load.
- ✗
Use streaming responses.
Why it's wrong here
Streaming improves time-to-first-token but not overall latency under peak load.
- ✗
Increase the max tokens parameter.
Why it's wrong here
Increasing max tokens can increase latency per request, worsening performance.
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