1Z0-1127-25 Using OCI Generative AI Service Practice Question
A company wants to use OCI Generative AI to summarize customer feedback. They need low latency and high throughput. Which configuration should they choose?
⚠ Common exam trap
Oracle often tests the misconception that fine-tuned models always outperform base models for latency, when in fact fine-tuning adds inference overhead that can degrade performance for high-throughput, low-latency use cases.
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
✓
Dedicated AI cluster with base model
Dedicated AI clusters provide guaranteed compute resources (GPUs) with no multi-tenant contention, ensuring low latency and high throughput for inference workloads. Using a base model avoids the additional overhead of fine-tuning inference, which can introduce latency due to custom weight loading and optimization steps. This combination is optimal for real-time summarization of customer feedback where response time and volume are critical.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Serverless endpoint with fine-tuned model
Why it's wrong here
Combines disadvantages of serverless and fine-tuning.
- ✓
Dedicated AI cluster with base model
Why this is correct
Correct: Dedicated resources ensure low latency and high throughput.
- ✗
Dedicated AI cluster with fine-tuned model
Why it's wrong here
Fine-tuning may not be necessary for summarization and could add overhead.
- ✗
Serverless endpoint with base model
Why it's wrong here
Serverless may have cold starts and limited throughput, not ideal for low latency.
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