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Why is understanding AI workloads becoming increasingly important on Windows devices?
Because AI workloads can only run on cloud infrastructure
Because organizations need insight into how AI applications use device resources and hardware accelerators
Because AI workloads replace traditional system monitoring
Because all Windows applications now require an NPU
While testing an AI-enabled application, Task Manager shows high CPU utilization but little or no NPU activity. The application documentation indicates that the feature normally uses the NPU on supported devices. What is the most reasonable conclusion?
The application has stopped functioning correctly
The workload might be using a fallback processing path and should be investigated further
The NPU hardware is damaged
The application is making effective use of the NPU
Which Task Manager tab provides a system-wide view of NPU and GPU utilization over time?
Startup apps
Processes
Details
Performance
Why might two applications that provide similar AI features have different effects on device performance and battery life?
Because AI workloads always use the same hardware resources
Because the hardware used to process AI workloads can have different performance and power characteristics
Because applications that use AI don't consume system resources
Because battery life is determined only by screen brightness
Why are NPUs important for supporting local AI workloads on Windows devices?
They provide additional storage capacity for AI models
They replace CPUs for all computing tasks
They are designed to perform AI computations efficiently, helping improve performance and power efficiency
They allow AI applications to run without memory resources
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