1Z0-1127-25 Using OCI Generative AI Service Practice Question
A company uses OCI Generative AI service with a Cohere Command model for a real-time chat application and experiences high latency. They have already set max_tokens to 50 and temperature to 0.2. Which further change would be most effective in reducing latency?
⚠ Common exam trap
A common mix-up: candidates confuse throughput optimization (asynchronous calls or more GPUs) with latency reduction, but for a single real-time request, model size is the dominant factor.
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
✓
Switch to a smaller model variant.
Switching to a smaller model variant (e.g., from Command to Command-Light) directly reduces the number of parameters and computational steps per token, which lowers inference latency. Since the company has already minimized max_tokens and temperature, the next most impactful change is to use a less resource-intensive model. This is a common optimization for real-time applications where response speed is 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.
- ✗
Use asynchronous invocation.
Why it's wrong here
Asynchronous invocation does not reduce the time to generate a response.
- ✓
Switch to a smaller model variant.
Why this is correct
Smaller models have fewer parameters and are faster.
- ✗
Disable context caching.
Why it's wrong here
Context caching can improve latency, not worsen it.
- ✗
Increase the number of GPUs.
Why it's wrong here
The user cannot control GPU allocation; it's managed by OCI.
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