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Free PDF Quiz 2025 Accurate NVIDIA Test NCP-AIO Duration

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NVIDIA NCP-AIO Exam Syllabus Topics:

Topic
Details

Topic 1

Installation and Deployment: This section of the exam measures the skills of system administrators and addresses core practices for installing and deploying infrastructure. Candidates are tested on installing and configuring Base Command Manager, initializing Kubernetes on NVIDIA hosts, and deploying containers from NVIDIA NGC as well as cloud VMI containers. The section also covers understanding storage requirements in AI data centers and deploying DOCA services on DPU Arm processors, ensuring robust setup of AI-driven environments.

Topic 2

Workload Management: This section of the exam measures the skills of AI infrastructure engineers and focuses on managing workloads effectively in AI environments. It evaluates the ability to administer Kubernetes clusters, maintain workload efficiency, and apply system management tools to troubleshoot operational issues. Emphasis is placed on ensuring that workloads run smoothly across different environments in alignment with NVIDIA technologies.

Topic 3

Administration: This section of the exam measures the skills of system administrators and covers essential tasks in managing AI workloads within data centers. Candidates are expected to understand fleet command, Slurm cluster management, and overall data center architecture specific to AI environments. It also includes knowledge of Base Command Manager (BCM), cluster provisioning, Run.ai administration, and configuration of Multi-Instance GPU (MIG) for both AI and high-performance computing applications.

Topic 4

Troubleshooting and Optimization: NVIThis section of the exam measures the skills of AI infrastructure engineers and focuses on diagnosing and resolving technical issues that arise in advanced AI systems. Topics include troubleshooting Docker, the Fabric Manager service for NVIDIA NVlink and NVSwitch systems, Base Command Manager, and Magnum IO components. Candidates must also demonstrate the ability to identify and solve storage performance issues, ensuring optimized performance across AI workloads.

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NVIDIA AI Operations Sample Questions (Q41-Q46):

NEW QUESTION # 41
You are configuring a storage system for storing the metadata associated with a large AI dataset. Metadata operations are I/O intensive but involve small files. Which storage solution is most appropriate for this scenario?

A. A traditional file server with spinning disks
B. A low-cost object storage service with high availability
C. A high-capacity HDD array in a RAID 0 configuration
D. A high-performance NVMe SSD RAID array optimized for random read/write operations
E. A tape library.

Answer: D

Explanation:
NVMe SSDs are ideal for metadata storage due to their low latency and high IOPS, which are crucial for handling numerous small file read/write operations. HDDs are slow, object storage introduces latency, and tape libraries are unsuitable for real-time access.

NEW QUESTION # 42
After updating the NVIDIA drivers on your NVSwitch-connected GPU server, 'nvsm' fails to start. The log file shows the following error: 'Failed to initialize NVML'. Which of the following actions is MOST likely to resolve the issue?

A. Increase the allocated memory to the 'nvsm' process.
B. Downgrade to the previous version of the NVIDIA drivers.
C. Disable SELinux.
D. Reinstall the operating system.
E. Ensure that the NVIDIA kernel modules are correctly loaded and that the CUDA toolkit is installed and configured properly.

Answer: E

Explanation:
NVML (NVIDIA Management Library) is a core component required for 'nvsm' to function. If NVML fails to initialize, it usually indicates a problem with the NVIDIA drivers, kernel modules, or CUDA installation. Verifying these components is the most direct way to resolve the issue. Downgrading can work but first you should verify your installation.

NEW QUESTION # 43
A user complains that their AI training job is running very slowly. Upon investigation, you discover that the pod is scheduled onto a node with a slow network connection, causing significant delays in data transfer. How would you ensure that future similar jobs are scheduled onto nodes with faster network connections?

A. Configure the kubelet to prioritize pods based on their network usage.
B. Implement node affinity rules based on network bandwidth labels, and label the nodes appropriately.
C. Increase the resource requests for the pod to trigger rescheduling.
D. Manually reschedule the pod onto a node with a faster network.
E. Use inter-pod affinity to force the job onto nodes already running network-intensive workloads.

Answer: B

Explanation:
The correct answer is B. By labeling nodes with their network bandwidth capabilities (e.g., 'network-bandwidth: 100GbpS), you can then use node affinity rules in your pod specifications to ensure that jobs requiring high bandwidth are scheduled onto suitable nodes. Option A is a temporary fix. Options C and D do not address the core issue of network bandwidth. Option E would exacerbate the problem by concentrating network-intensive workloads on the same nodes.

NEW QUESTION # 44
You have multiple users sharing a server with a single NVIDIAA100 GPU. Two users, Alice and Bob, want to run deep learning experiments concurrently. Alice's job requires 20GB of GPU memory and 30% of compute, while Bob's job needs IOGB of GPU memory and 20% of compute. How can you use MIG to optimally configure the GPU to accommodate both users' requirements?

A. Create two MIG instances: one lg.5gb instance for Alice and one lg.5gb instance for Bob.
B. Do not use MIG; let both users share the entire GPU.
C. Create one MIG instance for Alice and let Bob use the remaining GPU resources.
D. Create two MIG instances: one 4g.20gb instance for Alice and one 2g.10gb instance for Bob.
E. Create two MIG instances: one 3g.20gb instance for Alice and one lg.5gb instance for Bob.

Answer: D

Explanation:
This question challenges understanding of MIG instance sizes. Options A and B are not correct because they allocate insufficient memory to Alice. Option C is not correct because it does not provide dedicated resources for Bob. Option E means that Alice's job is resource intensive. The correct answer is D because it ensures that both Alice and Bob get at least the memory they need and some compute resource allocation. 4g.20gb and 2g.10gb instances ensure allocation of resources required for both users independently.

NEW QUESTION # 45
What is the primary advantage of using a disaggregated infrastructure for AI workloads compared to a traditional converged infrastructure?

A. Better security due to isolation.
B. Lower initial capital expenditure.
C. Reduced power consumption.
D. Simplified management and monitoring.
E. Independent scaling of compute, storage, and networking resources.

Answer: E

Explanation:
Disaggregated infrastructure allows you to scale compute, storage, and networking independently based on the specific needs of your AI workloads. Converged infrastructure typically scales in predefined units, which can lead to resource wastage. While disaggregation can lead to other benefits, independent scaling is the primary advantage for AI workloads with varying resource demands.

NEW QUESTION # 46
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