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An organization wants routine, supported AI workloads to run on its devices while continuing to use cloud AI for more complex scenarios. Which approach best aligns with the module?
Move every AI workload to local devices, regardless of its computing requirements.
Run appropriate workloads locally and reserve cloud AI for scenarios requiring additional scale or capabilities.
Use local processing only when a cloud AI service is unavailable.
Send routine workloads to the cloud and reserve device hardware for data storage.
A video editor regularly works with 8K footage, AI-assisted effects, HDR content, and multiple external displays. Which combination of Surface Laptop Ultra for Business features most directly supports this workflow?
Facial recognition, TPM 2.0, and a replaceable keyboard
Adaptive touch, a headphone port, and Microsoft Account backup
An SD card reader, Windows Update, and a Full HD front-facing camera
High Unified Memory configurations, fast SSD storage, advanced graphics, a color-accurate display, and multi-monitor support
A developer wants to evaluate supported local AI models by visually browsing a catalog, entering prompts, and comparing results without first building an application or using command-line tools. What should the developer use?
AI Dev Gallery
Windows Hello
Surface UEFI
The Surface app's Touchpad settings
An employee wants to customize the Surface haptic touchpad to meet their mobility and dexterity needs. Which feature should they enable?
Enhanced Sign-in Security
Automatic framing
Adaptive touch
Dynamic refresh rate
A data scientist is evaluating an AI workflow for a customer insights project. They want to compare outputs locally before deciding which parts of the solution require cloud-scale processing. Why is Surface Laptop Ultra for Business a strong fit for this scenario?
It can run suitable tests locally, helping the data scientist validate the workflow before scaling selected parts to the cloud.
It requires all model testing to be completed through a cloud-hosted service.
It automatically converts locally tested models into completed enterprise applications.
It removes the need to decide which workloads should run locally and which should move to the cloud.
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