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NVIDIA NCA-AIIO 認定試験の出題範囲：

トピック
出題範囲

トピック 1

• Essential AI knowledge: Exam Weight: This section of the exam measures the skills of IT professionals and covers foundational AI concepts. It includes understanding the NVIDIA software stack, differentiating between AI, machine learning, and deep learning, and comparing training versus inference. Key topics also involve explaining the factors behind AI's rapid adoption, identifying major AI use cases across industries, and describing the purpose of various NVIDIA solutions. The section requires knowledge of the software components in the AI development lifecycle and an ability to contrast GPU and CPU architectures.

トピック 2

• AI Operations: This section of the exam measures the skills of data center operators and encompasses the management of AI environments. It requires describing essentials for AI data center management, monitoring, and cluster orchestration. Key topics include articulating measures for monitoring GPUs, understanding job scheduling, and identifying considerations for virtualizing accelerated infrastructure. The operational knowledge also covers tools for orchestration and the principles of MLOps.

トピック 3

• AI Infrastructure: This section of the exam measures the skills of IT professionals and focuses on the physical and architectural components needed for AI. It involves understanding the process of extracting insights from large datasets through data mining and visualization. Candidates must be able to compare models using statistical metrics and identify data trends. The infrastructure knowledge extends to data center platforms, energy-efficient computing, networking for AI, and the role of technologies like NVIDIA DPUs in transforming data centers.

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NVIDIAのNCA-AIIO認定試験の合格証明書はあなたの仕事の上で更に一歩の昇進で生活条件が向上することが助けられます。NVIDIAのNCA-AIIO認定試験はIT専門知識のレベルの検査でPass4Testの専門IT専門家があなたのために最高で最も正確なNVIDIAのNCA-AIIO「NVIDIA-Certified Associate AI Infrastructure and Operations」試験資料が出来上がりました。Pass4Testは全面的な最高のNVIDIA NCA-AIIO試験の資料を含め、きっとあなたの最良の選択だと思います。

NVIDIA-Certified Associate AI Infrastructure and Operations 認定 NCA-AIIO 試験問題 (Q35-Q40):

質問 # 35
What is the primary advantage of using virtualized environments for AI workloads in a large enterprise setting?

• A. Reduces the need for specialized hardware by running AI workloads on general-purpose CPUs
• B. Allows for easier scaling of AI workloads across multiple physical machines
• C. Ensures that AI workloads are always running on the same physical machine for consistency
• D. Enables AI workloads to utilize cloud resources without requiring any changes to the underlying code

正解：B

解説：
Virtualized environments, such as those using NVIDIA vGPU or GPU passthrough, enable easier scaling of AI workloads across multiple physical machines by abstracting hardware resources. This allows enterprises to dynamically allocate GPUs to virtual machines (VMs) based on demand, supporting growth without physical reconfiguration. NVIDIA's virtualization solutions (e.g., GRID, vGPU Manager) integrate with platforms like VMware or Kubernetes, facilitating seamless scalingin data centers or hybrid clouds, a key advantage in enterprise AI deployments.
Option A is incorrect-AI workloads still require GPUs, not just CPUs. Option C contradicts virtualization's flexibility, as it doesn't tie workloads to one machine. Option D overstates compatibility; code may still need adjustments for cloud APIs. Scaling is the primary benefit, per NVIDIA's virtualization strategy.

質問 # 36
An AI operations team is tasked with monitoring a large-scale AI infrastructure where multiple GPUs are utilized in parallel. To ensure optimal performance and early detection of issues, which two criteria are essential for monitoring the GPUs? (Select two)

• A. Number of active CPU threads
• B. GPU fan noise levels
• C. Average CPU temperature
• D. GPU utilization percentage
• E. Memory bandwidth usage on GPUs

正解：D、E

解説：
For monitoring GPUs in an AI infrastructure:
* GPU utilization percentage(A) measures how effectively GPUs are being used, identifying underutilization or overloading-key to performance optimization.
* Memory bandwidth usage on GPUs(D) tracks data transfer rates within the GPU, critical for detecting bottlenecks in memory-intensive AI workloads like deep learning.
* Number of active CPU threads(B) is a CPU metric, less relevant to GPU performance.
* Average CPU temperature(C) monitors CPU health, not GPU status.
* GPU fan noise levels(E) are a byproduct, not a direct performance indicator.
NVIDIA's nvidia-smi tool provides these GPU metrics (A and D) for operational monitoring.

質問 # 37
When implementing an MLOps pipeline, which component is crucial for managing version control and tracking changes in model experiments?

• A. Orchestration Platform
• B. Continuous Integration (CI) System
• C. Artifact Repository
• D. Model Registry

正解：D

解説：
A Model Registry is crucial for managing version control and tracking changes in model experiments within an MLOps pipeline. It serves as a centralized repository to store, version, and manage trained models, their metadata (e.g., hyperparameters, performance metrics), and experiment history, ensuring reproducibility and governance. NVIDIA's AI Enterprise suite, including tools like NVIDIA NGC, supports model registries for streamlined MLOps. Option A (CI System) focuses on code integration, not model tracking. Option C (Orchestration Platform) manages workflows, not versioning. Option D (Artifact Repository) stores general outputs but lacks model-specific features. NVIDIA's MLOps documentation emphasizes the registry's role in AI lifecycle management.

質問 # 38
An autonomous vehicle company is developing a self-driving car that must detect and classify objects such as pedestrians, other vehicles, and traffic signs in real-time. The system needs to make split-second decisions based on complex visual data. Which approach should the company prioritize to effectively address this challenge?

• A. Apply a linear regression model to predict the position of objects based on camera inputs.
• B. Implement a deep learning model with convolutional neural networks (CNNs) to process and classify visual data.
• C. Develop an unsupervised learning algorithm to cluster visual data and classify objects based on similarity.
• D. Use a rule-based AI system to classify objects based on predefined visual characteristics.

正解：B

解説：
Real-time object detection and classification in autonomous vehicles require processing complex visual data (e.g., camera feeds) with high accuracy and minimal latency. Deep learning models with convolutional neural networks (CNNs) are the industry standard for this task, excelling at feature extraction and pattern recognition in images. NVIDIA's automotive solutions, like DRIVE AGX and TensorRT, optimize CNNs for real-time inference on GPUs, enabling split-second decisions critical for safety. For example, CNN-based models like YOLO or SSD, accelerated by NVIDIA GPUs, can detect and classify pedestrians, vehicles, and signs efficiently.
Unsupervised learning (Option A) is unsuitable for precise classification without labeled training data, which is essential for this use case. Linear regression (Option B) is too simplistic for multidimensional visual data, lacking the ability to handle complex patterns. Rule-based systems (Option C) are rigid and struggle with the variability of real-world scenarios, unlike adaptable CNNs. NVIDIA's focus on deep learning for autonomous driving underscores Option D as the prioritized approach.

質問 # 39
When extracting insights from large datasets using data mining and data visualization techniques, which of the following practices is most critical to ensure accurate and actionable results?

• A. Visualizing all possible data points in a single chart.
• B. Maximizing the size of the dataset used for training models.
• C. Using complex algorithms with the highest computational cost.
• D. Ensuring the data is cleaned and pre-processed appropriately.

正解：D

解説：
Accurate and actionable insights from data mining and visualization depend on high-quality data. Ensuring data is cleaned and pre-processed appropriately-removing noise, handling missing values, and normalizing features-prevents misleading results and ensures reliability. NVIDIA's RAPIDS library accelerates these steps on GPUs, enabling efficient preprocessing of large datasets for AI workflows, a critical practice in NVIDIA's data science ecosystem (e.g., DGX and NGC integrations).
Complex algorithms (Option A) may enhance analysis but are secondary to data quality; high cost doesn't guarantee accuracy. Visualizing all data points (Option C) can overwhelm charts, obscuring insights, and is less critical than preprocessing. Maximizing dataset size (Option D) can improve models but risks introducing noise if not cleaned, reducing actionability. NVIDIA's focus on data preparation in AI pipelines underscores Option B's importance.

質問 # 40
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