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NVIDIA NCP-AIO Exam Syllabus Topics:

Topic
Details

Topic 1

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 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

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 (Q62-Q67):

NEW QUESTION # 62
You're using Kubernetes with persistent volumes (PVs) backed by a network file system (NFS) to store your AI model checkpoints. You've noticed that checkpoint saving operations are slow and intermittently fail. After investigation, you suspect that the issue might be related to NFS locking. How can you diagnose and potentially resolve this issue?

A. Examine the logs of the NFS server and client for NFS lock-related errors or warnings (e.g., 'NFS: v4 operation not supported).
B. Ensure that the NFS server supports NFSv4 locking and that the client is configured to use NFSv4.
C. Increase the frequency of checkpoints to reduce the impact of individual checkpoint failures.
D. Switch to using a different storage backend that doesn't rely on NFS locking, such as a block storage device or an object storage system.
E. Try disabling NFS locking on the client side by using the 'nolock' mount option (with caution, as this can lead to data corruption if multiple clients write to the same file concurrently).

Answer: A,B,D,E

Explanation:
NFS locking issues often manifest as errors in server/client logs. Disabling locking can be a workaround, but with risk. Ensuring NFSv4 support and using alternative storage backends address the root cause.

NEW QUESTION # 63
You are the administrator of a Run.ai cluster with ACM enabled. You need to implement a chargeback mechanism to accurately track GPU usage and allocate costs to different research groups. What key pieces of information do you need to collect and what Run.ai and/or ACM features can help automate this process?

A. CPU utilization per job. This is the primary factor in determining costs.
B. Total number of jobs submitted by each group. Run.ai provides a summary of job submissions in the UI.
C. Network bandwidth used by each job. This is the best indicator of resource consumption.
D. GPU utilization per job, job duration, and associated research group. ACM and Run.ai provide APIs and dashboards for collecting this data, which can then be integrated with a billing system.
E. Average job completion time. Use this to distribute the cost equally.

Answer: D

Explanation:
For accurate chargeback, you need GPU utilization per job, job duration (to quantify resource usage over time), and the associated research group to whom the cost should be allocated. ACM and Run.ai provide APIs and dashboards for collecting this data, which can be integrated with a billing system for automated chargeback. While the total number of jobs submitted can be an indicator of activity, it doesn't reflect actual resource usage. CPU utilization and network bandwidth are less relevant than GPU utilization in a GPU-accelerated environment. Average job completion time is insufficient for equitable cost allocation.

NEW QUESTION # 64
You are deploying a PyTorch container from NGC that utilizes Tensor Cores. How can you verify that Tensor Cores are being effectively used during inference?

A. Analyze the training loss curve; a steep decline indicates Tensor Core usage.
B. Check the container logs for messages indicating Tensor Core usage.
C. Use the NVIDIA Nsight Systems profiler to analyze GPU kernel execution and identify Tensor Core operations.
D. Use the 'nvidia-smi' command to monitor GPU utilization and check for high Tensor Core activity.
E. Examine the CUDA code within the container to confirm explicit Tensor Core API calls.

Answer: C,D

Explanation:
B and E are correct. 'nvidia-smi' shows GPU utilization, including Tensor Core activity. Nsight Systems provides detailed profiling information, allowing you to identify specific Tensor Core operations. A is unreliable as log messages may not always be present. C refers to training, not inference. D is impractical without access to the container's source code.

NEW QUESTION # 65
You are using Ceph object storage to store your training dat
a. You observe that your training jobs are consistently slow, and monitoring tools indicate high latency when accessing the Ceph cluster. What are the possible causes that can contribute to this behavior?

A. The Ceph cluster's placement groups are not optimally configured for the workload, causing uneven data distribution.
B. Insufficient network bandwidth between the compute nodes and the Ceph cluster.
C. Insufficient CPU and Memory on the Ceph Monitors
D. OSDs (Object Storage Devices) in the Ceph cluster are overloaded, leading to slow read/write operations.
E. An incorrectly configured or malfunctioning Ceph monitor node.

Answer: A,B,D

Explanation:
High latency in Ceph can stem from several issues: network congestion limits data transfer, overloaded OSDs cannot handle the I/O load, and suboptimal placement groups lead to hotspots. A malfunctioning monitor would primarily affect cluster availability and metadata operations, not necessarily the data I/O performance directly. Insufficient CPU and Memory on OSD's as well may cause issues as well.

NEW QUESTION # 66
You are configuring BCM for cluster provisioning. You want to automate the installation of specific software packages on each newly provisioned node. How can you achieve this?

A. Create a Kubernetes Job that runs on each node to install the packages.
B. Specify the packages in the 'cluster.yamr file under the 'packages' section.
C. Include the package installation commands directly in the OS image.
D. Use a BCM post-provisioning script to install the packages.
E. Leverage a configuration management tool like Ansible or Chef within a BCM post-provisioning script.

Answer: C,D,E

Explanation:
Including packages in the OS image is a direct approach. Post-provisioning scripts allow customization after the base OS is installed. Configuration management tools offer more sophisticated automation. Kubernetes Jobs are designed for workload execution, not system-level package management. BCM does not have a 'packages' section in 'cluster.yamr for direct package specification.

NEW QUESTION # 67
......

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