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NVIDIA NCP-AIO Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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 3

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 4

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.

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Latest NCP-AIO Braindumps Questions & NCP-AIO Valid Braindumps Questions

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NVIDIA AI Operations Sample Questions (Q24-Q29):

NEW QUESTION # 24
You have an NVIDIAA100 GPU configured with MIG. After restarting the system, the MIG instances are no longer present. Which step is necessary to ensure MIG configurations persist after a reboot?

A. Save the MIG configuration to a file using 'nvidia-smi mig -SIP and load it on system startup.
B. The MIG configuration is stored in the BIOS; no additional steps are necessary.
C. Update the NVIDIA driver after each system restart.
D. Save the MIG configuration to the persistence database using 'nvidia-smi mig -Igip' . Also make sure you enable persistence mode.
E. Enable the 'MIG Persistence' option in the NVIDIA Control Panel.

Answer: D

Explanation:
MIG configurations are not persistent by default. You can use command to load and save instance placement to persistence DB (Igip). The '-Igip' option stores the configuration, and the '-elgip' option ensures it is loaded on system startup. Make sure you also enable persistence mode, so that the setting will survive a system restart.

NEW QUESTION # 25
While monitoring your storage system during a large training job, you notice consistently high disk I/O wait times ('iowait'). What does this metric indicate, and what actions can you take to mitigate it?

A. High 'iowait' indicates network congestion. Optimize network configuration.
B. High 'iowait' means the CPU is waiting for I/O operations to complete. Investigate storage performance bottlenecks such as disk saturation, network latency (if using networked storage), or inefficient data access patterns.
C. High 'iowait' means the system is swapping memory to disk. Add more RAM or reduce memory usage.
D. High 'iowait' is normal during large training jobs and does not require any action.
E. High 'iowait' means the CPU is waiting for I/O operations to complete. Increase CPU cores.

Answer: B

Explanation:
'iowait' directly reflects the time the CPU spends idle, waiting for disk I/O operations. The solutions are targetted to identify whether the bottleneck is disk saturation, network latency or inefficient data access patterns.

NEW QUESTION # 26
A system administrator needs to lower latency for an AI application by utilizing GPUDirect Storage.
What two (2) bottlenecks are avoided with this approach? (Choose two.)

A. NIC
B. System Memory
C. CPU
D. DPU
E. PCIe

Answer: B,C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
GPUDirect Storage allows data to be transferred directly from storage to GPU memory,bypassing the CPU and system memory. This reduces latency and overhead by avoiding data movement through the CPU and main memory, accelerating data feeding to GPUs for AI workloads. PCIe and NIC are still involved in the data path, and the DPU may participate depending on architecture but are not the primary bottlenecks avoided by GPUDirect Storage.

NEW QUESTION # 27
You have deployed a container from NGC running a large language model (LLM) for text generation. You notice that the container's performance degrades significantly over time. You suspect that GPU memory fragmentation is contributing to this issue. How can you diagnose and mitigate GPU memory fragmentation in this scenario?

A. Increase the container's memory limit to provide more space for memory allocation.
B. Monitor GPU memory usage with -nvidia-smi' and look for a high degree of fragmentation (small, non-contiguous memory blocks).
C. Use the function in PyTorch (if applicable) to release unused GPU memory.
D. Use CUDA memory pools to pre-allocate memory and reduce the frequency of memory allocations and deallocations.
E. Restart the container regularly to defragment the GPU memory.

Answer: B,C,D,E

Explanation:
'nvidia-smi' can reveal memory fragmentation. Restarting defragments the memory. CUDA memory pools minimize fragmentation. can release unused memory. D might delay the problem but doesn't address the root cause.

NEW QUESTION # 28
A user reports that their AI training job running on a BCM-managed cluster is experiencing slow 1/0 performance. What steps would you take to diagnose and resolve the issue, considering the potential involvement of storage?

A. Monitor the storage system's performance metrics (IOPS, latency, throughput) using the storage vendor's monitoring tools.
B. Increase the pod's CPU and memory limits to improve 1/0 performance.
C. Verify that the correct storage class is being used for the persistent volumes used by the training job.
D. Inspect the pod's logs for any I/O related errors or warnings.
E. Check the network bandwidth between the compute nodes and the storage system using *iperf or similar tools.

Answer: A,C,D,E

Explanation:
Network bandwidth can be a bottleneck. Storage system metrics are crucial for identifying storage-related issues. The storage class determines the underlying storage type and performance characteristics. Pod logs might contain error messages. Increasing CPU/memory won't directly solve I/O performance issues if the bottleneck is elsewhere.

NEW QUESTION # 29
......

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