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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: NVIDIA AI Infrastructure Components | 25-30% | - NVIDIA DGX systems - Storage solutions for AI workloads - NVIDIA networking solutions (Mellanox) - NVIDIA AI Enterprise software |
| Topic 2: AI Infrastructure Fundamentals | 15-20% | - NVIDIA software stack overview - Data center infrastructure requirements - GPU architecture basics - AI and Deep Learning concepts |
| Topic 3: Deployment and Configuration | 25-30% | - Software deployment - Cluster configuration - Network configuration - System installation and setup |
| Topic 4: Security and Best Practices | 10-15% | - Compliance considerations - Operational best practices - Security fundamentals |
| Topic 5: Monitoring and Management | 15-20% | - Performance monitoring - NVIDIA management tools - Resource utilization - Troubleshooting basics |
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NEW QUESTION # 41
One of the nodes in a cluster is not running as fast as the others and the system administrator needs to check the status of the GPUs on that system. What command should be used?
Answer: B
Explanation:
nvidia-smi is the standard NVIDIA command-line utility for checking GPU status, including utilization, temperature, power draw, memory usage, driver state, and running processes. It is the appropriate first command to investigate why a GPU node is performing slower than others.
NEW QUESTION # 42
A system administrator needs to change the RAID level of DGX station and use the script included in the DGX software with no data on the array. What are the options the administrator can choose?
Answer: A
Explanation:
The DGX Station RAID configuration script supports configuring the data array as RAID 0 or RAID 5. RAID 0 provides maximum performance and capacity, while RAID 5 provides fault tolerance with usable capacity tradeoff.
NEW QUESTION # 43
You are developing a CI/CD pipeline for deploying A1 models to a Kubernetes cluster with NVIDIA GPUs. Your pipeline includes steps to build, test, and deploy the model as a containerized application. What is the recommended approach for ensuring that the application container image includes the correct NVIDIA drivers and libraries?
Answer: E
Explanation:
Using a base image provided by NVIDIA (such as those from NGC) is the recommended approach because it ensures that the container image includes the correct and compatible drivers and libraries, simplifying the build process and reducing potential compatibility issues. The NVIDIA Container Toolkit requires host drivers and handles the injection of the library into the container and not the building of the image.
NEW QUESTION # 44
You are monitoring a server with 8 GPUs used for deep learning training. You observe that one of the GPUs reports a significantly lower utilization rate compared to the others, even though the workload is designed to distribute evenly. 'nvidia-smi' reports a persistent "XID 13" error for that GPU. What is the most likely cause?
Answer: E
Explanation:
XID 13 errors in 'nvidia-smi' typically indicate a hardware fault within the GPU. Driver bugs or memory issues would likely cause different error codes or system instability across multiple GPUs. CUDA version mismatch might prevent the application from running altogether, but is less likely to lead to a specific XID error on a single GPU. Exclusive Process mode will lead to it being used by a different process but not necessarily cause that XID error.
NEW QUESTION # 45
A leaf switch shows " FW Version Mismatch " alerts for transceivers after cluster expansion. Which tool validates transceiver firmware against expected versions?
Answer: A
Explanation:
Firmware consistency is a pillar of stable InfiniBand fabric performance. When a cluster is expanded, new transceivers or cables may arrive with newer or older firmware than the existing base, leading to " FW Version Mismatch " alerts in management consoles like UFM (Unified Fabric Manager). The flint tool (or mstflint) is the correct utility for querying the specific firmware levels embedded within the transceivers.
While iblinkinfo provides data on link speeds and port states, it does not provide the deep hardware-level firmware telemetry required for version validation. flint allows the administrator to query the device, compare the current burn version against the target image, and perform the necessary updates to bring the cluster into a uniform state. In NVIDIA AI infrastructure, maintaining uniform firmware across the fabric ensures that features like Adaptive Routing and Congestion Control operate predictably. Without version parity, inconsistent behavior in Forward Error Correction (FEC) or link-up negotiation can lead to intermittent performance drops that are difficult to diagnose at the application (NCCL) level.
NEW QUESTION # 46
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