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| Section | Weight | Objectives |
|---|---|---|
| AI Infrastructure Fundamentals | 15-20% | - AI and Deep Learning concepts - NVIDIA software stack overview - Data center infrastructure requirements - GPU architecture basics |
| NVIDIA AI Infrastructure Components | 25-30% | - NVIDIA networking solutions (Mellanox) - Storage solutions for AI workloads - NVIDIA AI Enterprise software - NVIDIA DGX systems |
| Security and Best Practices | 10-15% | - Compliance considerations - Operational best practices - Security fundamentals |
| Deployment and Configuration | 25-30% | - Cluster configuration - System installation and setup - Software deployment - Network configuration |
| Monitoring and Management | 15-20% | - Resource utilization - Troubleshooting basics - Performance monitoring - NVIDIA management tools |
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NEW QUESTION # 30
You are experiencing link flapping (frequent up/down transitions) on several InfiniBand links in your AI infrastructure. This is causing intermittent connectivity issues and performance degradation. What are the MOST likely causes of this issue, and what steps should you take to troubleshoot and resolve it? (Select TWO)
Answer: C,E
Explanation:
Link flapping is most commonly caused by physical layer issues (faulty cables, connectors, or transceivers) or configuration mismatches (link speeds or duplex settings). Troubleshooting should focus on inspecting the physical connections and verifying that the link speed and duplex settings are correctly configured on both ends of the link. While MTIJ issues and software bugs can cause network problems, they are less likely to directly cause link flapping. Excessive broadcast traffic can cause performance issues but is less likely to result in frequent link up/down transitions.
NEW QUESTION # 31
A system administrator needs to install a container toolkit and successfully run the following commands:
sudo apt-get update
sudo apt-get install -y nvidia-container-toolkit
sudo nvidia-ctk runtime configure --runtime docker
What step should be taken next to finish the installation?
Answer: C
Explanation:
The nvidia-ctk runtime configure command is a crucial step that modifies the Docker daemon configuration file (/etc/docker/daemon.json) to register the nvidia runtime. However, the Docker daemon only reads this configuration file during its initialization phase. Even though the toolkit is installed and the configuration file is updated, Docker will not be able to spawn GPU-accelerated containers until the service is refreshed.
Executing sudo systemctl restart docker (or the equivalent for your container engine) is the mandatory final step. This forces Docker to reload its settings and recognize the NVIDIA Container Runtime as a valid option.
Without this restart, attempting to run a container with the --gpus all flag will result in an error stating that the
"nvidia" runtime is not found or is unconfigured. This is a common point of failure in automated AI infrastructure deployments where the configuration script finishes, but the service state remains stale.
NEW QUESTION # 32
You are tasked with selecting transceivers for a new NVIDIA Quantum-2 InfiniBand switch deployment. The primary requirement is to minimize power consumption while maintaining 400Gbps bandwidth over short distances (up to 50 meters). Which transceiver type would offer the BEST power efficiency in this scenario?
Answer: B
Explanation:
SR4 transceivers are known for their relatively low power consumption compared to other 400GbE transceiver types. This is because they use a simpler modulation scheme and shorter reach, requiring less power for signal amplification and processing. LR8 and DR4 are designed for longer distances and consume more power. AOCs, while convenient, are not typically the most power-efficient option. SR8 may consume slightly higher power than SR4 but provides better performance in certain scenarios.
NEW QUESTION # 33
You are deploying a cloud-native AI inference service using Kubernetes and NVIDIA GPUs. You need to ensure that GPU resources are efficiently allocated and monitored. Which of the following approaches is MOST effective for achieving this within the Kubernetes environment?
Answer: D
Explanation:
The NVIDIA Device Plugin for Kubernetes is specifically designed to advertise GPU resources to the Kubernetes scheduler, enabling efficient allocation and utilization. Resource requests and limits ensure pods are scheduled on nodes with sufficient GPU capacity, preventing resource contention and 00M errors. Options A, C, D, and E are either ineffective, manual, or potentially lead to instability.
NEW QUESTION # 34
A system administrator lost SSH connectivity to a cluster management node. It has been identified that a network is broken. Which network is it?
Answer: B
Explanation:
SSH access to a cluster management node normally uses the in-band management network.
Losing SSH connectivity because of a broken network indicates a failure in the management path used for administrative login and cluster management access.
NEW QUESTION # 35
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