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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	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	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	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 (Q58-Q63):

NEW QUESTION # 58
You are managing a large Slurm cluster used for AI research. You notice that some users are submitting jobs that request excessive amounts of memory, even though their applications don't actually need it, leading to inefficient resource utilization. What steps can you take to address this issue and encourage users to request more appropriate memory resources?

A. Implement strict memory limits per job and automatically kill jobs that exceed those limits.
B. All of the above
C. Configure Slurm's accounting system to track memory usage and provide reports to users, highlighting inefficient memory requests.
D. Educate users on how to accurately estimate their memory requirements and provide tools for monitoring memory usage.
E. Adjust the scheduler's priority algorithm to penalize jobs that request excessive memory.

Answer: B

Explanation:
A multi-faceted approach is needed. Enforcing limits (A) prevents waste, education (B) helps users improve requests, accounting (C) provides feedback, and adjusting priority (D) discourages wasteful requests. A, B, C, D combined provide the best approach.

NEW QUESTION # 59
You're implementing a preemption policy in your Slurm cluster to allow higher-priority jobs to interrupt lower-priority jobs. Which Slurm configuration parameters are MOST relevant to configure preemption? (Select TWO)

A. FastSchedule
B. SchedulerRootFilter
C. PreemptMode
D. PreemptType
E. AccountingStorageType

Answer: C,D

Explanation:
'PreemptMode' defines when preemption is triggered (e.g., 'OFF', 'CANCEL', 'REQUEUE'). 'preemptType' determines which jobs are eligible for preemption (e.g., 'priority', 'qos').

NEW QUESTION # 60
A system administrator needs to collect the information below:
* GPU behavior monitoring
* GPU configuration management
* GPU policy oversight
* GPU health and diagnostics
* GPU accounting and process statistics
* NVSwitch configuration and monitoring
What single tool should be used?

A. Nsight Systems
B. CUDA Toolkit
C. nvidia-smi
D. DCGM

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
TheNVIDIA Data Center GPU Manager (DCGM)is the comprehensive management tool that provides all the requested functionalities: monitoring GPU behavior, managing configurations, enforcing policies, health diagnostics, process accounting, and NVSwitch monitoring. DCGM is designed for large-scale GPU management in data centers and AI clusters, providing detailed telemetry and control over NVIDIA GPUs and NVSwitches.
* nvidia-smiprovides GPU monitoring but lacks full policy and NVSwitch management.
* CUDA Toolkit is for GPU programming and development.
* Nsight Systems is focused on performance profiling and debugging.
Therefore, DCGM is the single tool that meets all the listed requirements.

NEW QUESTION # 61
You've deployed a container from NGC on a Kubernetes cluster, but the application is experiencing intermittent GPU errors. You suspect memory leaks within the container are causing the issue. What is the most effective method to diagnose this problem?

A. Use NVIDIA Nsight Systems to profile the application and identify memory allocation patterns.
B. Restart the container regularly to clear potential memory leaks.
C. Monitor the container's CPU usage using 'kubectl top pod'.
D. Analyze the application's logs for CUDA error messages related to memory allocation.
E. Use 'nvidia-smi' within the container to monitor GPU memory usage over time.

Answer: A,D,E

Explanation:
B, C, and E are correct. 'nvidia-smi' provides real-time GPU memory usage. Application logs often contain CUDA errors indicating memory issues. Nsight Systems offers detailed profiling to pinpoint memory leaks. A is not relevant to GPU memory leaks. D is a workaround, not a diagnostic solution.

NEW QUESTION # 62
Your Kubernetes cluster hosts several AI workloads with varying GPU requirements. Some workloads require high compute performance, while others are memory-bound. You want to optimize GPU resource allocation by bin-packing workloads with complementary resource profiles onto the same nodes. How would you approach this?

A. Employ a custom Kubernetes scheduler that considers GPU compute and memory usage when placing pods.
B. Manually assign workloads to specific nodes based on their resource requirements.
C. Configure resource quotas to limit the overall GPU usage in the cluster.
D. Implement a vertical pod autoscaler (VPA) to dynamically adjust the GPU resource requests of pods based on their actual usage.
E. Use node affinity rules to schedule all workloads onto the same set of nodes, regardless of their resource profiles.

Answer: A

Explanation:
The correct answer is B. A custom Kubernetes scheduler allows you to implement sophisticated bin-packing algorithms that consider both GPU compute and memory usage to optimize resource allocation. This enables you to pack workloads with complementary resource profiles onto the same nodes, maximizing GPU utilization. Option A ignores resource profiles. Option C is not scalable. Option D limits overall usage but doesn't optimize placement. Option E focuses on individual pod resource adjustment, not bin-packing.

NEW QUESTION # 63
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