NEW NCA-AIIO TEST QUESTIONS | NCA-AIIO RELIABLE TEST PRACTICE

New NCA-AIIO Test Questions | NCA-AIIO Reliable Test Practice

New NCA-AIIO Test Questions | NCA-AIIO Reliable Test Practice

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Tags: New NCA-AIIO Test Questions, NCA-AIIO Reliable Test Practice, NCA-AIIO Reliable Exam Bootcamp, Latest NCA-AIIO Exam Cram, Visual NCA-AIIO Cert Test

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NVIDIA-Certified Associate AI Infrastructure and Operations Sample Questions (Q38-Q43):

NEW QUESTION # 38
During AI model deployment, your team notices significant performance degradation in inference workloads.
The model is deployed on an NVIDIA GPU cluster with Kubernetes. Which of the following could be the most likely cause of the degradation?

  • A. CPU bottlenecks
  • B. High disk I/O latency
  • C. Insufficient GPU memory allocation
  • D. Outdated CUDA drivers

Answer: C

Explanation:
Insufficient GPU memory allocation is the most likely cause of inference degradation in a Kubernetes- managed NVIDIA GPU cluster. Memory shortages lead to swapping or failures, slowing performance. Option A (outdated CUDA) may cause compatibility issues, not direct degradation. Option B (CPU bottlenecks) affects preprocessing, not inference. Option C (disk I/O) impacts data loading, not GPU tasks. NVIDIA's Kubernetes GPU Operator docs stress memory allocation.


NEW QUESTION # 39
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. Power Usage
  • C. GPU Core Utilization
  • D. GPU Memory Usage

Answer: D

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 # 40
You are tasked with deploying multiple AI workloads in a data center that supports both virtualized and non- virtualized environments. To maximize resource efficiency and flexibility, which of the following strategies would be most effective for running AI workloads in a virtualized environment?

  • A. Deploy each AI workload in a separate virtual machine (VM) to isolate resources and prevent interference
  • B. Use a single VM to run all AI workloads sequentially, reducing the need for resource scheduling
  • C. Run all AI workloads on bare metal servers without virtualization to maximize performance
  • D. Use containerization within a single VM to run multiple AI workloads, leveraging shared resources efficiently

Answer: D

Explanation:
Using containerization within a single VM to run multiple AI workloads is the most effective strategy for maximizing resource efficiency and flexibility in a virtualized environment. Containers (e.g., Docker) allow multiple workloads to share GPU resources via NVIDIA's container runtime, offering lightweight isolation and efficient resource utilization compared to separate VMs. This approach, supported by NVIDIA's
"DeepOps" and "GPU Virtualization" documentation, leverages Kubernetes or similar orchestration for scalability and flexibility while maintaining performance on virtualized GPUs (e.g., via NVIDIA GPU Operator).
Separate VMs (B) waste resources due to overhead. Sequential execution in one VM (C) sacrificesparallelism, reducing efficiency. Bare metal (D) maximizes performance but lacks virtualization flexibility. NVIDIA recommends containerization for virtualized AI efficiency.


NEW QUESTION # 41
Your team is tasked with deploying a deep learning model that was trained on large datasets for natural language processing (NLP). The model will be used in a customer support chatbot, requiring fast, real-time responses. Which architectural considerations are most important when moving from the training environment to the inference environment?

  • A. Low-latency deployment and scaling
  • B. Data augmentation and hyperparameter tuning
  • C. High memory bandwidth and distributed training
  • D. Model checkpointing and distributed inference

Answer: A

Explanation:
Low-latency deployment and scaling are most important for an NLP chatbot requiring real-time responses.
This involves optimizing inference with tools like NVIDIA Triton and ensuring scalability for user demand.
Option A (augmentation, tuning) is training-focused. Option B (checkpointing) aids recovery, not latency.
Option D (memory, distributed training) suits training, not inference. NVIDIA's inference docs prioritize latency and scalability.


NEW QUESTION # 42
Which statement correctly differentiates between AI, machine learning, and deep learning?

  • A. Machine learning is a type of AI that only uses linear models, while deep learning involves non-linear models exclusively.
  • B. AI is a broad field encompassing various technologies, including machine learning, which focuses on data-driven models, and deep learning, a subset of machine learning using neural networks.
  • C. Machine learning is the same as AI, and deep learning is simply a method within AI that doesn't involve machine learning.
  • D. Deep learning is a broader concept than machine learning, which is a specialized form of AI.

Answer: B

Explanation:
AI is a broad field encompassing technologies for intelligent systems. Machine learning (ML), a subset, uses data-driven models, while deep learning (DL), a subset of ML, employs neural networks for complex tasks.
NVIDIA's ecosystem (e.g., cuDNN for DL, RAPIDS for ML) reflects this hierarchy, supporting all levels.
Option A misaligns ML and DL. Option C reverses the subset order. Option D oversimplifies ML and DL distinctions. Option B matches NVIDIA's conceptual framework.


NEW QUESTION # 43
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