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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Deployment and Configuration | 25-30% | - System installation and setup - Software deployment - Cluster configuration - Network configuration |
| Topic 2: AI Infrastructure Fundamentals | 15-20% | - GPU architecture basics - Data center infrastructure requirements - NVIDIA software stack overview - AI and Deep Learning concepts |
| Topic 3: Monitoring and Management | 15-20% | - Troubleshooting basics - NVIDIA management tools - Performance monitoring - Resource utilization |
| Topic 4: Security and Best Practices | 10-15% | - Operational best practices - Compliance considerations - Security fundamentals |
| Topic 5: NVIDIA AI Infrastructure Components | 25-30% | - Storage solutions for AI workloads - NVIDIA AI Enterprise software - NVIDIA networking solutions (Mellanox) - NVIDIA DGX systems |
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NEW QUESTION # 36
You are tasked with installing a DGX A100 server. After racking and connecting power and network cables, you power it on, but the BMC (Baseboard Management Controller) is not accessible via the network. You have verified the network cable is connected and the switch port is active. What are the MOST likely causes and initial troubleshooting steps you should take?
Answer: A,E
Explanation:
The most likely causes are network configuration issues (incorrect IP, subnet, or VLAN). The BMC requires a valid IP configuration and network connectivity to be accessible. While other options are possible, they are less common as initial causes.
NEW QUESTION # 37
After upgrading NVIDIA GPU Operator, Kubernetes reports all worker nodes as Ready, but newly created GPU workloads remain in the Pending state. The administrator confirms that GPUs are healthy and visible to the operating system. Which troubleshooting step should be performed first?
Answer: C
Explanation:
If GPUs are detected by the operating system but Kubernetes cannot schedule GPU workloads, the Device Plugin is one of the first components to verify. It advertises GPU resources to the Kubernetes scheduler. Healthy hardware alone does not guarantee that GPUs are available as schedulable resources within the cluster.
NEW QUESTION # 38
During a maintenance window, a system administrator needs to verify the CUDA version installed on the NVIDIA DGX server to ensure compatibility with applications. Which command could be used in the maintenance script to check the installed CUDA version?
Answer: B
Explanation:
dpkg -l | grep cuda lists installed CUDA-related packages on an Ubuntu-based DGX system, allowing the administrator to verify which CUDA toolkit/runtime versions are installed for application compatibility checks.
NEW QUESTION # 39
During a scale-out expansion, the AI Factory team needs to ensure minimal communication latency between GPUs across multiple nodes. Which NVIDIA technology should be prioritized in the network design to achieve this?
Answer: D
Explanation:
InfiniBand with a Fat-Tree topology is designed for low-latency, high-bandwidth scale-out GPU communication across multiple nodes. It supports efficient East-West traffic and helps minimize communication delays for distributed AI training workloads.
NEW QUESTION # 40
You are tasked with configuring an NVIDIAA100 GPU for a multi-tenant environment using MIG. The goal is to provide the following MIG configuration:
Answer: E
Explanation:
The question specifies that you need the configuration that allows 7x1g.10gb instances and one 7g.80gb. The format is num_1g/num_2g/num_3g/num_4g/num_7g/num_10g/num_20g/num_40g. In the provided configuration, 0/1/2/3/4/5/6 means one 7g, then we need the instances.
NEW QUESTION # 41
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