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| Topic | Details |
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| 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.
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| 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.
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| 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.
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| 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 (Q86-Q91):
NEW QUESTION # 86
You are deploying a VMI container using Kubernetes and want to ensure that your container is scheduled on a node with at least one NVIDIA GPU. Which Kubernetes feature is BEST suited for this requirement?
- A. Pod Disruption Budgets
- B. Taints and Tolerations
- C. Horizontal Pod Autoscaling
- D. Node Affinity
- E. Resource Quotas
Answer: D
Explanation:
Node Affinity allows you to specify rules for scheduling pods onto specific nodes based on labels or other node properties. In this case, you would use node affinity to target nodes with the 'nvidia.com/gpu' label.
NEW QUESTION # 87
You have configured MIG instances on an NVIDIA GPU. After a system reboot, the MIG configuration is lost, and all instances are gone. What is the MOST likely cause of this issue and how can you resolve it?
- A. The system BIOS does not support MIG. Update the BIOS to the latest version.
- B. MIG instances are automatically deleted after each reboot for security reasons.
- C. The MIG configuration was not saved persistently. Use 'nvidia-smi mig -Igip' to save the configuration to the persistence database after creation, then reboot.
- D. The system's power supply is insufficient. Use power supply with more wattage.
- E. The NVIDIA driver is outdated. Update the driver to the latest version.
Answer: C
Explanation:
MIG configurations are not persistent by default. The 'nvidia-smi mig -Igip' command can be used to load and save instance placement to persistence DB (Igip) and thus the instances are retained across reboots. If the configuration is not saved, it will be lost after a reboot. The other options are less likely causes of this specific issue.
NEW QUESTION # 88
Which two (2) ways does the pre-configured GPU Operator in NVIDIA Enterprise Catalog differ from the GPU Operator in the public NGC catalog? (Choose two.)
- A. It supports Mixed Strategies for Kubernetes deployments.
- B. It automatically installs the NVIDIA Datacenter driver.
- C. It is configured to use the NVIDIA License System (NLS).
- D. It additionally installs Network Operator.
- E. It is configured to use a prebuilt vGPU driver image.
Answer: C,E
Explanation:
The pre-configured GPU Operator in the NVIDIA Enterprise Catalog differs from the public NGC catalog GPU Operator primarily by its configuration to use a prebuilt vGPU driver image and being configured to use the NVIDIA License System (NLS). These adaptations allow better support for enterprise environments where vGPU functionality and license management are critical.
NEW QUESTION # 89
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. The MIG configuration is stored in the BIOS; no additional steps are necessary.
- B. Update the NVIDIA driver after each system restart.
- C. Save the MIG configuration to the persistence database using 'nvidia-smi mig -Igip' . Also make sure you enable persistence mode.
- D. Save the MIG configuration to a file using 'nvidia-smi mig -SIP and load it on system startup.
- E. Enable the 'MIG Persistence' option in the NVIDIA Control Panel.
Answer: C
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 # 90
What is the primary advantage of using a disaggregated infrastructure for AI workloads compared to a traditional converged infrastructure?
- A. Reduced power consumption.
- B. Lower initial capital expenditure.
- C. Better security due to isolation.
- 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 # 91
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