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Actual exam question for NVIDIA's NCA-AIIO exam Question #: 38 Topic #: 1
You are managing an AI infrastructure that supports a healthcare application requiring high availability and low latency. The system handles multiple workloads, including real-time diagnostics, patient data analysis, and predictive modeling for treatment outcomes. To ensure optimal performance, which strategy should you adopt for workload distribution and resource management?
In a healthcare application requiring high availability and low latency, such as one handling real-time diagnostics, patient data analysis, and predictive modeling, an auto-scaling strategy is critical. NVIDIA's AI infrastructure solutions, like those offered with NVIDIA DGX systems and NVIDIA AI Enterprise software, emphasize dynamic resource management to adapt to fluctuating workloads. Auto-scaling ensures that resources (e.g., GPU compute power, memory, and network bandwidth) are allocated based on real-time demand, which is essential for time-sensitive tasks like diagnostics that cannot tolerate delays. Option A (prioritizing diagnostics) might compromise other workloads like predictive modeling, leading to inefficiencies. Option B (manual allocation) is impractical for dynamic, unpredictable workloads, as it lacks adaptability and increases administrative overhead. Option D (equal allocation) fails to account for varying resource needs, potentially causing latency spikes in critical tasks. NVIDIA's documentation on AI Infrastructure for Enterprise highlights auto-scaling as a key feature for optimizing performance in hybrid and multi-workload environments, ensuring both high availability and low latency.
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