Evaluate representative quality against operational: Which evaluation conclusion is best supported?
Model choice must test representative quality together with resource, dependency, control, and correction constraints.
The question
A manufacturer evaluates models for detecting and describing factory defects. A small model fits local memory and latency limits but misses complex descriptions. A larger model improves descriptions but requires cloud processing, increasing dependency and data-control concerns. Production cannot pause for frequent manual correction. Which evaluation conclusion is best supported?
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- Use a benchmark-led selection, then address deployment risks separately.Benchmarks can inform selection, but separating them from deployment conditions risks choosing a model that cannot operate safely or reliably in the factory.
- Evaluate representative quality against operational and control constraints. ✓The evidence supports a use-case evaluation that measures defect quality together with latency, resource fit, cloud dependency, data controls, and correction capacity.
- Prefer the larger model when its descriptions score higher.Better descriptions matter, but a score does not resolve cloud dependency, data controls, factory limits, or the lack of correction capacity.
- Prefer the small model for local control.Local execution may reduce dependency exposure, but missed complex descriptions can make the model unsuitable for the production task.
The trap
Do not infer suitability from model size or one benchmark when deployment conditions conflict. How to remember it
Model choice must test representative quality together with resource, dependency, control, and correction constraints.
How many of these would you get right?
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Part of the Certsqill AIGP question bank · Understanding How to Govern AI Deployment and Use ·
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