Latest Test NCA-AIIO Discount - Dumps NCA-AIIO PDF

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NVIDIA-Certified Associate AI Infrastructure and Operations Sample Questions (Q15-Q20):

NEW QUESTION # 15
When virtualizing a GPU-accelerated infrastructure to support AI operations, what is a key factor to ensure efficient and scalable performance across virtual machines (VMs)?

A. Enable nested virtualization on the VMs.
B. Increase the CPU allocation to each VM.
C. Ensure that GPU memory is not overcommitted among VMs.
D. Allocate more network bandwidth to the host machine.

Answer: C

Explanation:
Ensuring that GPU memory is not overcommitted among VMs is a key factor for efficient and scalable performance in a virtualized GPU-accelerated infrastructure. NVIDIA's vGPU technology allows multiple VMs to share a GPU, but overcommitting memory (allocating more than physically available) causes contention, degrading performance. Proper memory allocation, as outlined in NVIDIA's vGPU documentation, ensures each VM has sufficient resources for AI workloads. Option A (more CPU) doesn't address GPU bottlenecks. Option C (network bandwidth) aids communication, not GPU efficiency. Option D (nested virtualization) adds complexity without direct benefit. NVIDIA emphasizes memory management for virtualization success.

NEW QUESTION # 16
When designing a data center specifically for AI workloads, which of the following factors is most critical to optimize for training large-scale neural networks?

A. Maximizing the number of storage arrays to handle data volumes
B. Ensuring the data center has a robust virtualization platform
C. High-speed, low-latency networking between compute nodes
D. Deploying the maximum number of CPU cores available in each node

Answer: C

Explanation:
High-speed, low-latency networking between compute nodes is the most critical factor to optimize when designing a data center for training large-scale neural networks. AI workloads, especially distributed training on NVIDIA GPUs (e.g., DGX systems), require rapid communication between nodes to exchange gradients, weights, and other data. Technologies like NVIDIA NVLink (intra-node) and InfiniBand or RDMA (inter- node) minimize communication overhead, ensuringscalability and reduced training time. NVIDIA's "DGX SuperPOD Reference Architecture" highlights that networking performance is a bottleneck in large-scale AI training, making it more critical than storage or CPU capacity.
Maximizing storage arrays (A) is important for data availability but less critical than networking for training performance. CPU cores (B) play a secondary role to GPUs in AI training. Virtualization (D) enhances flexibility but is not the primary optimization focus for training throughput. NVIDIA's AI infrastructure guidelines prioritize networking for such workloads.

NEW QUESTION # 17
You are managing an AI cluster where multiple jobs with varying resource demands are scheduled. Some jobs require exclusive GPU access, while others can share GPUs. Which of the following job scheduling strategies would best optimize GPU resource utilization across the cluster?

A. Increase the default pod resource requests in Kubernetes
B. Enable GPU sharing and use NVIDIA GPU Operator with Kubernetes
C. Schedule all jobs with dedicated GPU resources
D. Use FIFO (First In, First Out) Scheduling

Answer: B

Explanation:
Enabling GPU sharing and using NVIDIA GPU Operator with Kubernetes (C) optimizes resourceutilization by allowing flexible allocation of GPUs based on job requirements. The GPU Operator supports Multi- Instance GPU (MIG) mode on NVIDIA GPUs (e.g., A100), enabling jobs to share a single GPU when exclusive access isn't needed, while dedicating full GPUs to high-demand tasks. This dynamic scheduling, integrated with Kubernetes, balances utilization across the cluster efficiently.
* Dedicated GPU resources for all jobs(A) wastes capacity for shareable tasks, reducing efficiency.
* FIFO Scheduling(B) ignores resource demands, leading to suboptimal allocation.
* Increasing pod resource requests(D) may over-allocate resources, not addressing sharing or optimization.
NVIDIA's GPU Operator is designed for such mixed workloads (C).

NEW QUESTION # 18
Which of the following is a primary challenge when integrating AI into existing IT infrastructure?

A. Finding AI tools that are compatible with existing hardware
B. Ensuring AI models have a user-friendly interface
C. Selecting the right cloud service provider
D. Scalability of the AI workloads

Answer: D

Explanation:
Scalability of AI workloads is a primary challenge when integrating AI into existing IT infrastructure. AI tasks, especially training and inference on NVIDIA GPUs, demand significant compute, memory, and networking resources, which legacy systems may not handle efficiently. Scaling these workloads across clusters or hybrid environments requires careful planning, as noted in NVIDIA's "AI Infrastructure and Operations Fundamentals" and "AI Adoption Guide." User-friendly interfaces (A) are secondary to technical integration. Hardware compatibility (C) is less challenging with NVIDIA's broad support. Cloud provider selection (D) is a decision, not a core challenge.
NVIDIA identifies scalability as a key integration hurdle.

NEW QUESTION # 19
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?

A. PCIe Bandwidth Utilization
B. GPU Core Utilization
C. GPU Memory Usage
D. Power Usage

Answer: C

Explanation:
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.

NEW QUESTION # 20
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Latest Test NCA-AIIO Discount - Dumps NCA-AIIO PDF

100% Pass Quiz 2025 NVIDIA NCA-AIIO: NVIDIA-Certified Associate AI Infrastructure and Operations Newest Latest Test Discount

NVIDIA-Certified Associate AI Infrastructure and Operations Sample Questions (Q15-Q20):

NCA-AIIO Exam Preparation: NVIDIA-Certified Associate AI Infrastructure and Operations & NCA-AIIO Best Questions

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