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質問 # 62
Your AI infrastructure team is deploying a large NLP model on a Kubernetes cluster using NVIDIA GPUs.
The model inference requires low latency due to real-time user interaction. However, the team notices occasional latency spikes. What would be the most effective strategy to mitigate these latency spikes?
正解：D
解説：
Latency spikes in real-time NLP inference often result from variable request rates. NVIDIA Triton Inference Server with Dynamic Batching groups incoming requests into batches dynamically, smoothing out processing and reducing spikes on NVIDIA GPUs in a Kubernetes cluster (e.g., DGX). This ensures low latency, critical for user interaction.
MIG (Option A) isolates workloads but doesn't address batching. More replicas (Option C) scale throughput, not latency consistency. Quantization (Option D) speeds inference but may not eliminate spikes. Triton's dynamic batching is NVIDIA's solution for this.
質問 # 63
Your AI model training process suddenly slows down, and upon inspection, you notice that some of the GPUs in your multi-GPU setup are operating at full capacity while others are barely being used. What is the most likely cause of this imbalance?
正解：D
解説：
Uneven GPU utilization in a multi-GPU setup often stems from an imbalanced data loading process. In distributed training, if data isn't evenly distributed across GPUs (e.g., via data parallelism), some GPUs receive more work while others idle, causing performance slowdowns. NVIDIA's NCCL ensures efficient communication between GPUs, but it relies on the data pipeline-managed by tools like NVIDIA DALI or PyTorch DataLoader-to distribute batches uniformly. A bottleneck in data loading, such as slow I/O or poor partitioning, is a common culprit, detectable via NVIDIA profiling tools like Nsight Systems.
Model code optimized for specific GPUs (Option A) is unlikely unless explicitly written to exclude certain GPUs, which is rare. Different GPU models (Option B) can cause imbalances due to varying capabilities, but NVIDIA frameworks typically handle heterogeneity; this would be a design flaw, not a sudden issue.
Improper installation (Option C) would likely cause complete failures, not partial utilization. Data distribution is the most probable and fixable cause, per NVIDIA's distributed training best practices.
質問 # 64
Which of the following best describes how memory and storage requirements differ between training and inference in AI systems?
正解：B
解説：
Training and inference have distinct resource demands in AI systems. Training involves processing large datasets, computing gradients, and updating model weights, requiring significant memory (e.g., GPU VRAM) for intermediate tensors and storage for datasets and checkpoints. NVIDIA GPUs like the A100 with HBM3 memory are designed to handle these demands, often paired with high-capacity NVMe storage in DGX systems. Inference, conversely, uses a pre-trained model to make predictions, requiring less memory (only the model and input data) and minimal storage, focusing on low latency and throughput.
Option A is incorrect-training's iterative nature demands more resources than inference's single-pass execution. Option C is false; inference rarely loads multiple models at once unless explicitly designed that way, and its memory needs are lower. Option D reverses the reality-training needs substantial memory, not minimal, while inference prioritizes speed over storage. NVIDIA's documentation on training (e.g., DGX) versus inference (e.g., TensorRT) workloads confirms Option B.
質問 # 65
Your AI infrastructure team is observing out-of-memory (OOM) errors during the execution of large deep learning models on NVIDIA GPUs. To prevent these errors and optimize model performance, which GPU monitoring metric is most critical?
正解：D
解説：
GPU Memory Usage is the most critical metric to monitor to prevent out-of-memory (OOM) errors and optimize performance for large deep learning models on NVIDIA GPUs. OOM errors occur when a model's memory requirements (e.g., weights, activations) exceed the GPU's available memory (e.g., 40GB on A100).
Monitoring memory usage with tools like NVIDIA DCGM helps identify when limits are approached, enabling adjustments like reducing batch size or enabling mixed precision, as emphasized in NVIDIA's
"DCGM User Guide" and "AI Infrastructure and Operations Fundamentals."
Core utilization (B) tracks compute load, not memory. Power usage (C) relates to efficiency, not OOM. PCIe bandwidth (D) affects data transfer, not memory capacity. Memory usage is NVIDIA's key metric for OOM prevention.
質問 # 66
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?
正解：A
解説：
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.
質問 # 67
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GoShikenがもっと早くNVIDIAのNCA-AIIO認証試験に合格させるサイトで、NVIDIAのNCA-AIIO「NVIDIA-Certified Associate AI Infrastructure and Operations」認証試験についての問題集が市場にどんどん湧いてきます。GoShikenを選択したら、成功をとりましょう。
NCA-AIIO専門トレーリング: https://www.goshiken.com/NVIDIA/NCA-AIIO-mondaishu.html
NVIDIA NCA-AIIO日本語版トレーリング そうすれば、自分の能力を有る分野で証明できます、私たちNVIDIA NCA-AIIO専門トレーリングは、NCA-AIIO専門トレーリング - NVIDIA-Certified Associate AI Infrastructure and Operationsトレーニングクイズについて、クライアントの質問や疑問を返信し、その問題を解決するため、24時間オンライン顧客サービスを提供しています、時間のペースを保ち、絶えず変化し、自分自身に挑戦したい場合は、1種類のNCA-AIIO証明書テストに参加して、実用的な能力を向上させ、知識の量を増やしてください、NVIDIA NCA-AIIO日本語版トレーリング ほかの人はあなたの成績に驚いているとき、ひょっとしたら、あなたはよりよい仕事を探しましたかもしれません、NVIDIA NCA-AIIO日本語版トレーリング 受験者が試験に合格しない確率は低いですが、私たちはあなたに十分な準備をします。
最初は川柳や標語そしてエッセーといった比較的短い文章を募集しているのを選び、片っ端NCA-AIIOから応募した、どうしたことかと下（しも）の女中に聞くと、 姫君が昨晩にわかにお亡（かく）れになりましたので、女房がたはだれも気を失ったようになっていらっしゃるのですよ。
そうすれば、自分の能力を有る分野で証明できます、私たちNVIDIAはNCA-AIIO認証pdf資料、NVIDIA-Certified Associate AI Infrastructure and Operationsトレーニングクイズについて、クライアントの質問や疑問を返信し、その問題を解決するため、24時間オンライン顧客サービスを提供しています。
時間のペースを保ち、絶えず変化し、自分自身に挑戦したい場合は、1種類のNCA-AIIO証明書テストに参加して、実用的な能力を向上させ、知識の量を増やしてください、ほかの人はあなたの成績に驚いているとき、ひょっとしたら、あなたはよりよい仕事を探しましたかもしれません。
受験者が試験に合格しない確率は低いですが、私たちはあなたに十分な準備をします。
