Evaluate an edge-capable deployment: Which deployment distinction best fits these constraints, while
Edge deployment may address latency and outages, but testing and human approval remain necessary for sensitive adverse decisions.
The question
A public-benefit agency must classify incoming applications within two seconds, including during intermittent network outages. The model processes sensitive case data, and staff must approve adverse eligibility actions. Which deployment distinction best fits these constraints, while preserving required controls?
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- Use a local model because local storage automatically satisfies governance requirements.Local processing can improve availability and control, but location alone does not satisfy privacy, security, validation, or oversight requirements.
- Use a cloud model because centralized infrastructure simplifies maintenance.Central maintenance may help, but outage sensitivity and latency requirements make connectivity and dependency risks unresolved.
- Evaluate an edge-capable deployment, then validate latency, privacy, and review controls. ✓Edge processing may address latency and outages, but suitability still depends on testing data controls and preserving human approval.
- Use an open model on agency servers to avoid external dependencies entirely.An open model may reduce some supplier dependence, but agency operation still requires evaluation, security, monitoring, and approval controls.
The trap
Choose the architecture that addresses the stated constraint, then retain contextual testing and consequential-action oversight. How to remember it
Edge deployment may address latency and outages, but testing and human approval remain necessary for sensitive adverse decisions.
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 ·
Every answer, right and wrong, comes with its own explanation.
