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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: NVIDIA AI Infrastructure Components | 25-30% | - Storage solutions for AI workloads - NVIDIA AI Enterprise software - NVIDIA DGX systems - NVIDIA networking solutions (Mellanox) |
| Topic 2: AI Infrastructure Fundamentals | 15-20% | - NVIDIA software stack overview - GPU architecture basics - Data center infrastructure requirements - AI and Deep Learning concepts |
| Topic 3: Security and Best Practices | 10-15% | - Compliance considerations - Security fundamentals - Operational best practices |
| Topic 4: Monitoring and Management | 15-20% | - Troubleshooting basics - Resource utilization - NVIDIA management tools - Performance monitoring |
| Topic 5: Deployment and Configuration | 25-30% | - Software deployment - Cluster configuration - Network configuration - System installation and setup |
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NEW QUESTION # 59
An engineer wants to verify that an NVIDIA GPU is accessible inside a Docker container for running deep learning workloads. The NVIDIA Container Toolkit is installed on a machine with working NVIDIA drivers.
Which command demonstrates the correct way to run a container that can access all available GPUs?
Answer: B
Explanation:
The correct command is docker run --rm --gpus all nvidia/cuda:12.4.6-base-ubuntu22.04 nvidia-smi. With the NVIDIA Container Toolkit installed and Docker configured, the --gpus all flag is the standard way to expose all available NVIDIA GPUs to a container. Running nvidia-smi inside the CUDA base container confirms that the container can see the host GPU driver stack, GPU devices, and NVIDIA runtime integration. Option A is incorrect because --runtime=docker is not the correct NVIDIA runtime configuration and does not request GPU access. Option B uses a plain Ubuntu container, which normally lacks the NVIDIA user-space libraries needed to run nvidia-smi. Option D uses an NVIDIA CUDA image but does not include the --gpus all flag, so Docker will not automatically expose GPUs to the container. In NVIDIA AI infrastructure, this validation is a basic control-plane and container-runtime check before running AI workloads through Docker, Kubernetes, Slurm containers, or NVIDIA GPU Operator-managed environments.
NEW QUESTION # 60
Your deep learning training job that utilizes NCCL (NVIDIA Collective Communications Library) for multi-GPU communication is failing with "NCCL internal error, unhandled system error" after a recent CUDA update. The error occurs during the 'all reduce' operation.
What is the most likely root cause and how would you address it?
Answer: B
Explanation:
NCCL relies on specific CUDA versions. An incompatibility after a CUDA update is the most probable cause. Insufficient shared memory is less likely to cause a system error within NCCL. Firewall rules usually manifest as connection refused errors. Faulty network cables affect inter-node communication, not intra-node. While RDMA issues can cause problems, they typically don't present as 'unhandled system error' immediately after a CUDA update, and are more likely if RDMA was working previously.
NEW QUESTION # 61
A user encounters "permission denied" errors when running GPU-accelerated containers on a Secure Boot- enabled system. What resolves this?
Answer: B
Explanation:
On systems where UEFI Secure Boot is enabled, the Linux kernel strictly enforces a "Signature Verification" policy for all loaded kernel modules. The NVIDIA driver consists of several modules (like nvidia.ko and nvidia-uvm.ko) that provide the interface between the hardware and the NVIDIA Container Runtime. If these modules are not signed by a key trusted by the system's firmware, the kernel will block them from loading, leading to "Permission Denied" errors when the container attempts to access /dev/nvidiactl or the GPU device nodes. To resolve this without compromising the security of the host, the administrator must utilize aMachine Owner Key (MOK). The modules must be signed using this key, and the key itself must be "Enrolled" into the system's Secure Boot database via the MOK management interface during the boot process. This establishes a "Chain of Trust" that allows the drivers to function. Disabling SELinux (Option C) or using sudo (Option D) will not resolve this, as the blockage is happening at the kernel-firmware interface level, not at the user-space permission level.
NEW QUESTION # 62
You are using MIG (Multi-lnstance GPU) on an NVIDIAAI 00 GPU within a Kubernetes cluster. You want to configure a pod to use a specific MIG instance. How do you define the GPU resource request in the pod's YAML definition?
Answer: E
Explanation:
When using MIG, you need to specify the MIG device profile name in the resource request to allocate a specific MIG instance to the pod. For example, 'nvidia.com/mig-1g.7gb: 1' requests one instance of the lg.7gb MIG profile. Specifying the full GPU ID will not work with MIG. Generic resource requests do not work with MIG.
NEW QUESTION # 63
A DGX H100 system shows intermittent "Link Down" errors on a 2006 DAC cable. CVT reports
"No Signal" despite physical connection. What is the first hardware check?
Answer: C
Explanation:
A CVT "No Signal" result with intermittent link-down events points first to a physical-layer problem. The initial hardware check should confirm that the DAC is supported for the ConnectX-
7/DGX H100 platform and inspect the cable ends, ports, and connectors for damage, seating issues, or incompatibility before changing speed, FEC, or switch hardware.
NEW QUESTION # 64
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