NCA-AIIO VALID BRAINDUMPS FILES - EXAM NCA-AIIO VOUCHER

NCA-AIIO Valid Braindumps Files - Exam NCA-AIIO Voucher

NCA-AIIO Valid Braindumps Files - Exam NCA-AIIO Voucher

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NVIDIA-Certified Associate AI Infrastructure and Operations Sample Questions (Q27-Q32):

NEW QUESTION # 27
In an effort to improve energy efficiency in your AI infrastructure using NVIDIA GPUs, you're considering several strategies. Which of the following would most effectively balance energy efficiency with maintaining performance?

  • A. Disabling all energy-saving features to ensure maximum performance
  • B. Enabling deep sleep mode on all GPUs during processing times
  • C. Running all GPUs at the lowest possible clock speeds
  • D. Employing NVIDIA GPU Boost technology to dynamically adjust clock speeds

Answer: D

Explanation:
Employing NVIDIA GPU Boost technology to dynamically adjust clock speeds is the most effective strategy to balance energy efficiency and performance in an AI infrastructure. GPU Boost, available on NVIDIA GPUs like A100, adjusts clock speeds and voltage based on workload demands and thermal conditions, optimizing Performance Per Watt. This ensures high performance when needed while reducing power use during lighter loads, as detailed in NVIDIA's "GPU Boost Documentation" and "AI Infrastructure for Enterprise." Deep sleep mode (A) during processing disrupts performance. Disabling energy-saving features (B) wastes power. Lowest clock speeds (C) sacrifice performance unnecessarily. GPU Boost is NVIDIA's recommended approach for efficiency.


NEW QUESTION # 28
Which NVIDIA software component is specifically designed to accelerate the end-to-end data science workflow by leveraging GPU acceleration?

  • A. NVIDIA RAPIDS
  • B. NVIDIA DeepStream SDK
  • C. NVIDIA CUDA Toolkit
  • D. NVIDIA TensorRT

Answer: A

Explanation:
NVIDIA RAPIDS is a suite of GPU-accelerated libraries (e.g., cuDF, cuML) designed to speed up the end-to- end data science workflow, from data preparation to machine learning, on NVIDIA GPUs. It integrates with tools like Pandas and Scikit-learn, providing dramatic performance boosts for tasks like ETL, feature engineering, and model training, as used in DGX systems and cloud environments.
The CUDA Toolkit (Option A) is a general-purpose GPU programming platform, not data science-specific.
DeepStream SDK (Option B) targets video analytics, not broad data science. TensorRT (Option C) optimizes inference, not the full workflow. RAPIDS is NVIDIA's dedicated data science accelerator.


NEW QUESTION # 29
In your AI data center, you need to ensure continuous performance and reliability across all operations. Which two strategies are most critical for effective monitoring? (Select two)

  • A. Implementing predictive maintenance based on historical hardware performance data
  • B. Deploying a comprehensive monitoring system that includes real-time metrics on CPU, GPU, and memory usage
  • C. Using manual logs to track system performance daily
  • D. Disabling non-essential monitoring to reduce system overhead
  • E. Conducting weekly performance reviews without real-time monitoring

Answer: A,B

Explanation:
For continuous performance and reliability:
* Deploying a comprehensive monitoring system(D) with real-time metrics (e.g., CPU/GPU usage, memory, temperature via nvidia-smi) enables immediate detection of issues, ensuring optimal operation in an AI data center.
* Implementing predictive maintenance(E) uses historical data (e.g., failure patterns) to anticipate and prevent hardware issues, enhancing reliability proactively.
* Weekly reviews(A) lack real-time responsiveness, risking downtime.
* Manual logs(B) are slow and error-prone, unfit for continuous monitoring.
* Disabling monitoring(C) reduces overhead but blinds operations to issues.
NVIDIA's monitoring tools support D and E as best practices.


NEW QUESTION # 30
You are configuring a multi-node AI training environment using NVIDIA GPUs, and your team wants to ensure that the network infrastructure can handle the data transfer between nodes efficiently, especially during distributed training tasks. What is the most critical factor to consider in the network infrastructure to minimize bottlenecks during distributed AI training?

  • A. Using software-defined networking (SDN) to manage traffic
  • B. Increasing the number of Ethernet ports on each node
  • C. Implementing InfiniBand with RDMA support
  • D. Reducing the number of nodes to simplify the network

Answer: C

Explanation:
Implementing InfiniBand with RDMA support is the most critical factor to minimize bottlenecks in distributed AI training. It provides ultra-low latency and high bandwidth (e.g., 200 Gb/s), optimizing GPU-to- GPU data transfers via NCCL. Option B (more Ethernet ports) improves redundancy, not speed. Option C (fewer nodes) limits scalability. Option D (SDN) aids management, not raw performance. NVIDIA's DGX networking guides recommend InfiniBand.


NEW QUESTION # 31
In an AI data center, you are working with a professional administrator to optimize the deployment of AI workloads across multiple servers. Which of the following actions would best contribute to improving the efficiency and performance of the data center?

  • A. Consolidate all AI workloads onto a single high-performance server to maximize GPU utilization
  • B. Distribute AI workloads across multiple servers with GPUs, while using DPUs to manage network and storage tasks
  • C. [Note: Original question only provided three options; assuming a typo and treating A as the intended correct answer]
  • D. Allocate all networking tasks to the CPUs, allowing the GPUs and DPUs to focus solely on AI model computation

Answer: B

Explanation:
Distributing AI workloads across multiple servers with GPUs, while using DPUs (e.g., NVIDIA BlueField) to manage network and storage tasks, best improves efficiency and performance in an AI data center. This approach leverages GPU parallelism for computation and offloads networking/storage (e.g., RDMA, encryption) to DPUs, reducing CPU overhead and latency. NVIDIA's "BlueField DPU Documentation" and
"AI Infrastructure for Enterprise" highlight this as an optimized design for scalable, high-performance AI deployments.
Consolidating workloads on one server (B) creates a bottleneck and single point of failure. Assigning networking to CPUs (C) negates DPU benefits, reducing efficiency. NVIDIA's architecture guidance supports distributed GPU-DPU setups.


NEW QUESTION # 32
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