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| Section | Weight | Objectives |
|---|---|---|
| Monitoring and Management | 15-20% | - Troubleshooting basics - NVIDIA management tools - Resource utilization - Performance monitoring |
| Deployment and Configuration | 25-30% | - Cluster configuration - Software deployment - Network configuration - System installation and setup |
| AI Infrastructure Fundamentals | 15-20% | - Data center infrastructure requirements - GPU architecture basics - AI and Deep Learning concepts - NVIDIA software stack overview |
| Security and Best Practices | 10-15% | - Security fundamentals - Operational best practices - Compliance considerations |
| NVIDIA AI Infrastructure Components | 25-30% | - Storage solutions for AI workloads - NVIDIA AI Enterprise software - NVIDIA DGX systems - NVIDIA networking solutions (Mellanox) |
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NEW QUESTION # 71
You are managing a cluster of servers with NVIDIA GPUs. You want to automate the process of installing and updating NVIDIA drivers across all nodes. Which of the following approaches would be the most scalable and maintainable?
Answer: B,D
Explanation:
Configuration management tools (Ansible, Chef, Puppet) provide the most scalable and maintainable solution. They allow you to define the desired state of the system and automatically enforce it across all nodes. NVIDIA's container toolkit encapsulates the driver and provides a consistent, isolated environment. Manually SSHing is not scalable. While 'pdsh' and 'parallel' can automate the execution of commands, they lack the robust error handling and state management of configuration management tools. NFS distribution requires managing the installation process separately on each server.
NEW QUESTION # 72
A user wants to restrict a Docker container to use only GPUs 0 and 2. Which command achieves this?
Answer: C
Explanation:
With the advent of the NVIDIA Container Toolkit and modern Docker versions (19.03+), the --gpus flag is the official, verified method for resource allocation. To restrict a container to specific hardware IDs, the syntax requires a specific string format: --gpus '"device=0,2"'. This tells the NVIDIA Container Runtime to map only those specific physical GPU devices into the container's namespace. While environment variables like NVIDIA_VISIBLE_DEVICES (Option B) were used in older "nvidia-docker2" setups, they are now considered legacy and can be overridden by the more modern --gpus flag. Option D is incorrect because simply mapping the device nodes (/dev/nvidiaX) is insufficient; the container also needs the appropriate volume mounts for the NVIDIA drivers and libraries, which the --gpus flag handles automatically. This precise isolation is critical in multi-tenant AI environments to ensure that a single developer or job doesn't accidentally utilize the entire 8-GPU tray of a DGX H100.
NEW QUESTION # 73
You are validating the environment of an NVIDIA GPU-accelerated data center during post-deployment checks. Which one action is essential to confirm that power and cooling are sufficient for the stable operation of NVIDIA DGX H100 systems?
Answer: A
Explanation:
Stable operation of high-density AI infrastructure like the DGX H100 requires strict adherence to power and thermal specifications. A single DGX H100 system can draw up to 10.2kW under peak load. Therefore, the most essential validation step is ensuring the electrical " infrastructure-to-server " handoff is healthy. This involves verifying that the system is connected to redundant PDUs (Power Distribution Units) capable of handling the amperage requirements without tripping breakers. Using NVSM (NVIDIA System Management), an administrator must check that all six power supplies (PSUs) are functional and receiving nominal input voltage (typically 200V-240V). If a PSU reports sub-optimal input or a " Loss of Redundancy,
" the system may throttle performance or shut down unexpectedly during a heavy training run. Fans running at 100% (Option A) at all times would actually indicate an inefficient or failed cooling policy, as fans should dynamically scale based on thermals. Overclocking (Option B) is not supported or recommended for enterprise DGX systems, as they are already factory-tuned for the highest stable performance.
NEW QUESTION # 74
Which of the following statements are correct regarding the use of NVIDIA GPUs with Docker containers?
Answer: B,C,E
Explanation:
The NVIDIA Container Toolkit allows GPU-accelerated apps to run in Docker without altering the image. The host's drivers are leveraged. CUDA libraries are necessary inside the container if your app uses CUDA. is used to control GPU visibility within the container. Drivers are not needed inside the container because they're managed by the host (making B incorrect), and 'nvidia-smi' can be run inside containers if the NVIDIA Container Toolkit is properly set up (making C incorrect).
NEW QUESTION # 75
You're setting up a cluster with 8 NVIDIA A100 GPUs. Each GPU needs to read 4GB/s from storage to keep it fully utilized. The network connecting the storage and compute nodes has a bandwidth of 25GB/s. What is the maximum number of GPUs that can be simultaneously saturated with data without exceeding the network bandwidth?
Answer: D
Explanation:
Each GPU requires 4GB/s of bandwidth. The network has a bandwidth of 25GB/s. Therefore, the maximum number of GPUs that can be saturated is 25GB/s / 4GB/s per GPU = 6.25 GPUs. Since you can't have a fraction of a GPU, the answer is 6 GPUs.
NEW QUESTION # 76
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