2025 NVIDIA NCA-AIIO REALISTIC EXAM DETAILS PASS GUARANTEED

2025 NVIDIA NCA-AIIO Realistic Exam Details Pass Guaranteed

2025 NVIDIA NCA-AIIO Realistic Exam Details Pass Guaranteed

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Tags: Exam NCA-AIIO Details, Latest NCA-AIIO Test Guide, NCA-AIIO Test Simulator Fee, NCA-AIIO Practice Exam Questions, NCA-AIIO Valid Test Question

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NVIDIA-Certified Associate AI Infrastructure and Operations Sample Questions (Q128-Q133):

NEW QUESTION # 128
A financial services company is developing a machine learning model to detect fraudulent transactions in real- time. They need to manage the entire AI lifecycle, from data preprocessing to model deployment and monitoring. Which combination of NVIDIA software components should they integrate to ensure an efficient and scalable AI development and deployment process?

  • A. NVIDIA DeepStream for data processing, CUDA for model training, and NGC for deployment.
  • B. NVIDIA Metropolis for data collection, DIGITS for training, and Triton Inference Server for deployment.
  • C. NVIDIA RAPIDS for data processing, TensorRT for model optimization, and Triton Inference Server for deployment.
  • D. NVIDIA Clara for model training, TensorRT for data processing, and Jetson for deployment.

Answer: C

Explanation:
The AI lifecycle for real-time fraud detection needs efficient data preprocessing, model optimization, and deployment. NVIDIA RAPIDS accelerates data processing on GPUs, TensorRToptimizes models for low- latency inference, and Triton Inference Server scales deployment across platforms-perfect for financial use cases in NVIDIA DGX or cloud environments.
Clara (Option A) is healthcare-focused, not fraud. DeepStream (Option C) is video-centric, and CUDA isn't a full training solution. Metropolis (Option D) targets smart cities, and DIGITS is outdated. Option B aligns with NVIDIA's lifecycle strategy.


NEW QUESTION # 129
Which of the following statements best explains why AI workloads are more effectively handled by distributed computing environments?

  • A. Distributed computing environments allow parallel processing of AI tasks, speeding up training and inference.
  • B. AI models are inherently simpler, making them well-suited to distributed environments.
  • C. AI workloads require less memory than traditional workloads, which is best managed by distributed systems.
  • D. Distributed systems reduce the need for specialized hardware like GPUs.

Answer: A

Explanation:
AI workloads, particularly deep learning tasks, involve massive datasets and complex computations (e.g., matrix multiplications) that benefit significantly from parallel processing. Distributed computing environments, such as multi-GPU or multi-node clusters, allow these tasks to be split across multiple compute resources, reducing training and inference times. NVIDIA's technologies, like NVIDIA Collective Communications Library (NCCL) and NVLink, enable high-speed communication between GPUs, facilitating efficient parallelization. For example, during training, data parallelism splits the dataset across GPUs, while model parallelism divides the model itself,both of which accelerate processing.
Option B is incorrect because AI models are not inherently simpler; they are often highly complex, requiring significant computational power. Option C is false as distributed systems typically rely on specialized hardware like NVIDIA GPUs to achieve high performance, not reduce their need. Option D is also incorrect- AI workloads often demand substantial memory (e.g., for large models like transformers), and distributed systems help manage this by pooling resources, not because the memory requirement is low. NVIDIA DGX systems and cloud offerings like DGX Cloud exemplify how distributed computing enhances AI workload efficiency.


NEW QUESTION # 130
You are responsible for managing an AI data center that handles large-scale deep learning workloads. The performance of your training jobs has recently degraded, and you've noticed that the GPUs are underutilized while CPU usage remains high. Which of the following actions would most likely resolve this issue?

  • A. Increase the GPU memory allocation.
  • B. Add more GPUs to the system.
  • C. Reduce the batch size during training.
  • D. Optimize the data pipeline for better I/O throughput.

Answer: D

Explanation:
GPU underutilization with high CPU usage during training suggests a bottleneck in the data pipeline, where CPUs can't feed data to GPUs fast enough, starving them of work. Optimizing the data pipeline for better I/O throughput-using NVIDIA DALI for GPU-accelerated data loading or improving storage (e.g., NVMe SSDs)
-ensures data reaches GPUs efficiently, maximizing utilization. This is a common issue in NVIDIA DGX systems, where pipeline optimization is critical for large-scale workloads.
Increasing GPU memory (Option A) doesn't address data delivery. Reducing batch size (Option B) might lower GPU demand but reduces throughput, not solving the root cause. Adding GPUs (Option C) exacerbates underutilization without fixing the bottleneck. NVIDIA's training optimization guides prioritize pipeline efficiency.


NEW QUESTION # 131
Which two software components are directly involved in the life cycle of AI development and deployment, particularly in model training and model serving? (Select two)

  • A. MLflow
  • B. Prometheus
  • C. Apache Spark
  • D. Kubeflow
  • E. Airflow

Answer: A,D

Explanation:
MLflow (B) and Kubeflow (E) are directly involved in the AI development and deployment life cycle, particularly for model training and serving. MLflow is an open-source platform for managing the ML lifecycle, including experiment tracking, model training, and deployment, often used with NVIDIA GPUs.
Kubeflow is a Kubernetes-native toolkit for orchestrating AI workflows, supporting training (e.g., via TFJob) and serving (e.g., with Triton), as noted in NVIDIA's "DeepOps" and "AI Infrastructure and Operations Fundamentals." Prometheus (A) is for monitoring, not AI lifecycle tasks. Airflow (C) manages workflows but isn't AI- specific. Apache Spark (D) processes data but isn't focused on model serving. NVIDIA's ecosystem integrates MLflow and Kubeflow for AI workflows.


NEW QUESTION # 132
Which NVIDIA solution is specifically designed to accelerate the development and deployment of AI in healthcare, particularly in medical imaging and genomics?

  • A. NVIDIA Metropolis
  • B. NVIDIA Clara
  • C. NVIDIA TensorRT
  • D. NVIDIA Jetson

Answer: B

Explanation:
NVIDIA Clara is specifically designed to accelerate AI development and deployment in healthcare, focusing on medical imaging and genomics with tools like Clara Imaging and Clara Genomics. Option A (Jetson) targets edge AI. Option B (TensorRT) optimizes inference broadly. Option C (Metropolis) focuses on smart cities. NVIDIA's Clara documentation confirms its healthcare specialization.


NEW QUESTION # 133
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