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NVIDIA NCA-AIIO New Dumps Questions - New NCA-AIIO Exam Practice

This format enables you to assess your NCA-AIIO test preparation with a NCA-AIIO practice exam. You can also customize your time and the kinds of questions of the NVIDIA NCA-AIIO Practice Test. This NVIDIA-Certified Associate AI Infrastructure and Operations NCA-AIIO practice test imitates the NVIDIA NCA-AIIO real exam pattern. Thus, it helps you kill NVIDIA-Certified Associate AI Infrastructure and Operations exam anxiety.

NVIDIA NCA-AIIO Exam Syllabus Topics:

Topic
Details

Topic 1

Essential AI Knowledge: This section of the exam measures the skills of IT professionals and covers the foundational concepts of artificial intelligence. Candidates are expected to understand NVIDIA's software stack, distinguish between AI, machine learning, and deep learning, and identify use cases and industry applications of AI. It also covers the roles of CPUs and GPUs, recent technological advancements, and the AI development lifecycle. The objective is to ensure professionals grasp how to align AI capabilities with enterprise needs.

Topic 2

AI Infrastructure: This part of the exam evaluates the capabilities of Data Center Technicians and focuses on extracting insights from large datasets using data analysis and visualization techniques. It involves understanding performance metrics, visual representation of findings, and identifying patterns in data. It emphasizes familiarity with high-performance AI infrastructure including NVIDIA GPUs, DPUs, and network elements necessary for energy-efficient, scalable, and high-density AI environments, both on-prem and in the cloud.

Topic 3

AI Operations: This domain assesses the operational understanding of IT professionals and focuses on managing AI environments efficiently. It includes essentials of data center monitoring, job scheduling, and cluster orchestration. The section also ensures that candidates can monitor GPU usage, manage containers and virtualized infrastructure, and utilize NVIDIA’s tools such as Base Command and DCGM to support stable AI operations in enterprise setups.

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NVIDIA-Certified Associate AI Infrastructure and Operations Sample Questions (Q34-Q39):

NEW QUESTION # 34
A global financial institution is implementing an AI-driven fraud detection system that must process vast amounts of transaction data in real-time across multiple regions. The system needs to be highly scalable, maintain low latency, and ensure data security and compliance with various international regulations. The infrastructure should also support continuous model updates without disrupting the service. Which combination of NVIDIA technologies would best meet the requirements for this fraud detection system?

A. Implement the system on NVIDIA Quadro GPUs with TensorFlow for model training and deployment.
B. Deploy the system on NVIDIA DGX A100 systems with NVIDIA Merlin for real-time data processing and model updates.
C. Use NVIDIA Jetson AGX Xavier devices for distributed data processing across regional offices.
D. Deploy the system on generic CPU-based servers with CUDA for accelerated computation.

Answer: B

Explanation:
Deploying on NVIDIA DGX A100 systems with NVIDIA Merlin best meets the requirements for ascalable, low-latency, secure fraud detection system with continuous updates. DGX A100 provides high-performance GPU compute (e.g., 5 petaFLOPS AI performance) for real-time processing and training, while Merlin accelerates recommendation and fraud detection workflows with real-time feature engineering and model updates, ensuring minimal disruption. Option A (Quadro GPUs) lacks the scalability of DGX. Option C (CPU- based with CUDA) underutilizes GPU potential. Option D (Jetson AGX) suits edge, not centralized, processing. NVIDIA's financial use case documentation supports this combination.

NEW QUESTION # 35
Which of the following NVIDIA compute platforms is best suited for deploying AI workloads at the edge with minimal latency?

A. NVIDIA GRID
B. NVIDIA Jetson
C. NVIDIA Tesla
D. NVIDIA RTX

Answer: B

Explanation:
NVIDIA Jetson (D) is best suited for deploying AI workloads at the edge with minimal latency. The Jetson family (e.g., Jetson Nano, AGX Xavier) is designed for compact, power-efficient edge computing, delivering real-time AI inference for applications like IoT, robotics, and autonomous systems. It integrates GPU, CPU, and I/O in a single module, optimized for low-latency processing on-site.
* NVIDIA GRID(A) is for virtualized GPU sharing, not edge deployment.
* NVIDIA Tesla(B) is a data center GPU, too power-hungry for edge use.
* NVIDIA RTX(C) targets gaming/workstations, not edge-specific needs.
Jetson's edge focus is well-documented by NVIDIA (D).

NEW QUESTION # 36
Your AI team notices that the training jobs on your NVIDIA GPU cluster are taking longer than expected.
Upon investigation, you suspect underutilization of the GPUs. Which monitoring metric is the most critical to determine if the GPUs are being underutilized?

A. GPU Utilization Percentage
B. Network Latency
C. CPU Utilization
D. Memory Bandwidth Utilization

Answer: A

Explanation:
GPU Utilization Percentage is the most direct metric to assess whether GPUs are underutilized during training. Measured as a percentage of time the GPU is actively processing tasks, it's available via NVIDIA tools like nvidia-smi and DCGM (Data Center GPU Manager). A low percentage (e.g., below 70-80% during training) indicates the GPU isn't fully engaged, often due to bottlenecks like slow data loading or inefficient parallelism, common issues in NVIDIA GPU clusters (e.g., DGX systems). This metric pinpoints the root cause of prolonged training times.
Memory Bandwidth Utilization (Option B) shows memory usage efficiency but not overall GPU activity.
Network Latency (Option C) affects multi-node setups but isn't a primary indicator of single-GPU utilization.
CPU Utilization (Option D) reflects CPU load, not GPU performance. NVIDIA's performance tuning guides prioritize GPU Utilization for diagnosing underutilization.

NEW QUESTION # 37
In an AI cluster, what is the purpose of job scheduling?

A. To assign workloads to available compute resources.
B. To gather and analyze cluster data on a regular schedule.
C. To install, update, and configure cluster software.
D. To monitor and troubleshoot cluster performance.

Answer: A

Explanation:
Job scheduling in an AI cluster assigns workloads (e.g., training, inference) to available compute resources (GPUs, CPUs), optimizing resource utilization and ensuring efficient execution. It's distinct from data analysis, monitoring, or software management, focusing solely on workload distribution.
(Reference: NVIDIA AI Infrastructure and Operations Study Guide, Section on Job Scheduling)

NEW QUESTION # 38
You are managing an AI infrastructure that includes multiple NVIDIA GPUs across various virtual machines (VMs) in a cloud environment. One of the VMs is consistently underperforming compared to others, even though it has the same GPU allocation and is running similar workloads.What is the most likely cause of the underperformance in this virtual machine?

A. Insufficient CPU allocation for the VM
B. Misconfigured GPU passthrough settings
C. Inadequate storage I/O performance
D. Incorrect GPU driver version installed

Answer: B

Explanation:
In a virtualized cloud environment with NVIDIA GPUs, underperformance in one VM despite identical GPU allocation suggests a configuration issue. Misconfigured GPU passthrough settings-where the GPU isn't directly accessible to the VM due to improper hypervisor setup (e.g., PCIe passthrough in KVM or VMware)
-is the most likely cause. NVIDIA's vGPU or passthrough documentation stresses correct configuration for full GPU performance; errors here limit the VM's access to GPU resources, causing slowdowns.
Inadequate storage I/O (Option B) or CPU allocation (Option C) could affect performance but would likely impact all VMs similarly if uniform. An incorrect GPU driver (Option D) might cause failures, not just underperformance, and is less likely in a managed cloud. Passthrough misalignment is a common NVIDIA virtualization issue.

NEW QUESTION # 39
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