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| Section | Weight | Objectives |
|---|---|---|
| NVIDIA AI Infrastructure Components | 25-30% | - NVIDIA AI Enterprise software - NVIDIA networking solutions (Mellanox) - Storage solutions for AI workloads - NVIDIA DGX systems |
| Security and Best Practices | 10-15% | - Compliance considerations - Security fundamentals - Operational best practices |
| Deployment and Configuration | 25-30% | - Network configuration - Software deployment - System installation and setup - Cluster configuration |
| AI Infrastructure Fundamentals | 15-20% | - GPU architecture basics - AI and Deep Learning concepts - NVIDIA software stack overview - Data center infrastructure requirements |
| Monitoring and Management | 15-20% | - NVIDIA management tools - Performance monitoring - Troubleshooting basics - Resource utilization |
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NEW QUESTION # 59
A network engineer is tasked with configuring the management, storage, and compute networks for a new DGX BasePOD deployment. Which statement best describes the network segmentation required for optimal operation?
Answer: B
Explanation:
DGX BasePOD deployments use separate network segments for distinct traffic classes so that management access, out-of-band hardware management, storage traffic, and high-performance compute communication do not interfere with each other. This segmentation improves performance, isolation, troubleshooting, and operational reliability.
NEW QUESTION # 60
You are tasked with installing a BlueField-2 DPU on a server. After physical installation, the DPU is not recognized by the host OS (Linux). You've verified the power and connection. What is the most likely first step you should take to troubleshoot the issue?
Answer: E
Explanation:
IOMMU is crucial for the host OS to properly recognize and manage the DPUs resources. If IOMMIJ is disabled or misconfigured, the DPU might not be detected. The other options are less likely to be the immediate cause of the issue. While drivers are important, IOMMIJ is a fundamental requirement. IJEFI config is also imporant but less likely.
NEW QUESTION # 61
You are tasked with setting up High Availability (HA) for NVIDIA Base Command Manager (BCM) in a new GPU cluster. The cluster consists of a primary head node, a secondary head node, and several compute nodes. The requirements are automatic failover of BCM services, minimal disruption to workloads, and proper cluster health monitoring during and after installation. During your BCM HA installation and configuration process, which two of the following actions are mandatory for ensuring a robust and verified HA cluster configuration?
Pick the 2 correct responses below.
Answer: C,E
Explanation:
A floating Virtual IP address is required because users, compute nodes, and management services need a consistent endpoint for the active BCM head node. NVIDIA DGX BasePOD HA documentation states that the external VIP is the IP address that should always be used to access the active head node after HA configuration is complete. The same HA workflow also verifies failover behavior after setup by checking HA status from both directions and confirming which head node is active. Therefore, a controlled failover validation is necessary to prove that the secondary head node can assume service responsibility. Compute nodes do not need to be powered on and running workloads during HA setup; NVIDIA documentation actually shows cluster nodes being powered off before HA configuration. Independent static IP addresses alone do not provide transparent service continuity because clients would need to reconnect manually.
Synchronization occurs as part of the HA setup workflow, but the best two answers for automatic failover and verified readiness are the floating VIP and failover validation.
NEW QUESTION # 62
A media company is developing an AI platform for video content analysis that requires storing and processing large volumes of unstructured video data. The platform must support high throughput for data ingestion and provide efficient access for real-time analytics. Given these requirements, which storage strategy should the company implement?
Answer: B
Explanation:
While object storage is excellent for massive scale and metadata, NVIDIA AI infrastructure best practices for training workloads-especially video analysis-heavily prioritizeParallel File Systems (PFS). Modern AI frameworks (PyTorch, TensorFlow) and NVIDIA's own SDKs (like DeepStream or NeMo) are optimized to read from POSIX-compliant file systems. For video content analysis, the training process involves "sharding" large video files and performing random-access reads across a massive dataset. A high-performance file system (such as Lustre, Weka, or IBM Storage Scale) provides the high throughput and low-latency metadata operations required to keep 8 or more H100 GPUs per node saturated with data. File storage allows for the hierarchical organization that data scientists use to manage datasets (e.g., /datasets/train/videos/) and supports GPUDirect Storage (GDS), which allows the GPU to pull data directly from the storage fabric into GPU memory, bypassing the CPU to maximize ingestion throughput.
NEW QUESTION # 63
A DGX A100 server with dual power supplies reports a critical power event in the BMC logs. One PSU shows a 'degraded' status, while the other appears normal. What immediate actions should you take to ensure continued operation and prevent data loss?
Answer: B,E
Explanation:
Hot-swapping the degraded PSU (B) restores redundancy. Migrating workloads (E) minimizes the risk of data loss or service interruption if the remaining PSU fails. Shutting down the server (A) causes unnecessary downtime if hot-swapping is possible. Monitoring the remaining PSU (C) is a good practice, but it's not a replacement for restoring redundancy or mitigating risk. Reducing GPU power limits (D) may help prevent further strain but is a temporary solution that impacts performance.
NEW QUESTION # 64
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