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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

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.

Topic 3

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 4

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.

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NVIDIA AI Operations Sample Questions (Q21-Q26):

NEW QUESTION # 21
You are tasked with optimizing the performance of a distributed deep learning training job running on multiple nodes interconnected with InfiniBand. You suspect that network communication is a bottleneck. Which tools and techniques would be MOST effective for diagnosing the issue?

A. Use 'ibstat' to check the status of the InfiniBand interfaces and identify any link errors or congestion.
B. Monitor network bandwidth utilization with tools like 'iperf3' to measure the actual throughput between nodes.
C. Check the CPU utilization on each node using 'top'.
D. Use 'nvidia-smi' to monitor GPU utilization on each node.
E. Employ network profiling tools like 'mpiP' or NVIDIA Nsight Systems to analyze MPI communication patterns and identify bottlenecks.

Answer: A,B,E

Explanation:
'ibstat' (A) provides direct insight into the InfiniBand link status. Network profiling tools (B) offer detailed analysis of MPI communication. Bandwidth monitoring tools (C) measure actual network throughput. While GPU (D) and CPU (E) utilization are important, they don't directly diagnose network bottlenecks.

NEW QUESTION # 22
You are designing a data center network to support distributed deep learning training across multiple servers. The training job uses NCCL (NVIDIA Collective Communications Library) for inter-GPU communication. Which of the following network configurations will maximize the performance of NCCL?

A. A Clos network topology with non-blocking links between all servers, utilizing RoCEv2 or InfiniBand.
B. A traditional three-tier network architecture with oversubscribed links at each layer.
C. A network using only TCP/IP without RDMA support.
D. A VLAN-based network with no QOS (Quality of Service) configured.
E. A single network switch connecting all servers, with each server connected via a single IOGbE link.

Answer: A

Explanation:
NCCL benefits greatly from low-latency, high-bandwidth communication. A Clos network with non-blocking links, RoCEv2, or InfiniBand ensures that GPUs can communicate efficiently without bottlenecks. A single switch with limited bandwidth, a three-tier network with oversubscription, or lack of RDMA will significantly hinder NCCL performance. VLANs without QOS do not guarantee low latency.

NEW QUESTION # 23
In a high availability (HA) cluster, you need to ensure that split-brain scenarios are avoided.
What is a common technique used to prevent split-brain in an HA cluster?

A. Using multiple load balancers to distribute traffic evenly across nodes.
B. Configuring manual failover procedures for each node.
C. Implementing a heartbeat network between cluster nodes to monitor their health.
D. Replicating data across all nodes in real time.

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Aheartbeat networkis a common technique used in HA clusters to continuously monitor the health and availability of cluster nodes. It allows nodes to detect failures and coordinate failover actions, thus preventing split-brain scenarios where multiple nodes believe they are active simultaneously, causing data corruption or conflicts. Manual failover, load balancers, or data replication alone do not prevent split-brain without this monitoring mechanism.

NEW QUESTION # 24
You need to monitor the GPU utilization of individual MIG instances on your NVIDIAA100 GPU. Which of the following tools or methods can provide granular monitoring data for each MIG instance?

A. The 'top' command in Linux provides GPU utilization information.
B. Use the Windows Task Manager to view GPU utilization.
C. DCGM (Data Center GPU Manager) provides detailed monitoring metrics for individual MIG instances.
D. nvidia-smi' alone, without any specific flags, provides per-MIG instance utilization.
E. The 'free command in Linux provides GPU memory usage information.

Answer: C

Explanation:
DCGM is a comprehensive tool for monitoring NVIDIA GPUs in data centers. It provides granular metrics for individual MIG instances, including GPU utilization, memory usage, and power consumption. While 'nvidia-smi' can display MIG information, it's limited without DCGM for detailed monitoring.

NEW QUESTION # 25
You are managing a cluster with multiple NVIDIA GPUs. A user reports that their deep learning training job is running slower than expected. Which of the following system management tools would provide the MOST direct insight into potential GPU bottlenecks?

A. 'top' command in Linux
B. aux' command in Linux
C. 'df -m command in Linux
D. 'free -m' command in Linux
E. nvidia-smi' (NVIDIA System Management Interface)

Answer: E

Explanation:
'nvidia-smi' is specifically designed to monitor NVIDIA GPUs, providing information on utilization, memory usage, temperature, and power consumption, allowing for quick identification of GPU-related bottlenecks. The other tools provide system-level information but not GPU- specific details.

NEW QUESTION # 26
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