引导式测验 · 硬 等级

边缘人工智能测验

通过本地与远程任务、设备资源、持续测试、压缩检查、隐私流和安全更新计划来评估边缘人工智能。

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问题 1 的 8

Which deployment is an example of edge AI?

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  1. Which deployment is an example of edge AI?
    • Processing close to where data is collected or used, such as a phone or local gateway
    • Training a model only on a central server
    • Using a larger model than the cloud
    • Disabling all connectivity
  2. An application must work offline. What should its design include?
    • An assumption that every service is local
    • A useful failure mode for functions that still need remote retrieval
    • A larger model by default
    • A permanent network connection
  3. An edge feature has 2 GB available, 1.2 GB of weights, and 0.6 GB of temporary buffers. What remains before other feature needs?
    • 0.8 GB, because only weights count
    • 1.4 GB, because weights use no runtime memory
    • 0.2 GB, leaving little margin for other needs
    • 2 GB, because buffers are outside the budget
  4. A short device benchmark looks good. What could it miss?
    • Only the original model download time
    • Only the peak arithmetic rating
    • Only the model weight-file size
    • Thermal throttling and competition with other applications over time
  5. After quantizing a model to fit a device, what should be checked?
    • Only the new file size
    • Task performance on difficult inputs and intended environmental conditions
    • Whether the model loads once
    • A benchmark from another device
  6. A phone performs local inference but syncs logs to a server. What does this imply for privacy review?
    • Privacy review is unnecessary
    • Local inference means no data leaves the device
    • Logs and synchronization still need controls
    • The app is fully offline
  7. Why does the guide recommend versioning models?
    • To identify which model version ran on each device
    • To prevent all updates
    • To hide device resource use
    • To change the battery capacity
  8. What should an update plan include for intermittently connected devices?
    • Deleting the previous version before validation
    • A requirement that devices never reconnect
    • An unverified moving model
    • A safe update or rollback path