A systems administrator is preparing a new DGX server for deployment. What is the most secure approach to configuring the BMC port during initial setup?
Correct Answer: D
Explanation:
The Baseboard Management Controller (BMC) is a powerful tool that allows for total control over the DGX system, including the ability to flash firmware, cycle power, and access the serial console. Because of this, it is a high-value target for security threats. The " 100% verified " secure approach (Option D) involves two critical layers: Network Isolation: The BMC port should never be exposed to the public internet (Option A) or even the general production network (Option B). It must reside on a dedicated Out-of-Band (OOB) network that is firewalled and accessible only to authorized administrators. Credential Management: Standard NVIDIA factory defaults (like admin/admin) must be changed immediately upon first access. As part of the DGX first-boot wizard, the system prompts the administrator to create a strong, unique password for the primary user, which is then synchronized to the BMC. Leaving the port disconnected (Option C) is unfeasible for modern data center operations, as the BMC is required for remote monitoring and " headless " deployment. Following the isolated/firewalled approach ensures the AI Factory remains resilient against both external attacks and internal lateral movement.
Question 2
A team is installing the NVIDIA Run:ai control plane on a Kubernetes cluster. Which two (2) options are most critical to validate before proceeding? (Pick the 2 correct responses below)
Correct Answer: A, B
Explanation:
NVIDIA Run:ai is an advanced orchestration platform designed to optimize GPU resource allocation within Kubernetes environments. Because Run:ai is cloud-native, its control plane and worker agents are deployed as Kubernetes resources. Therefore, the absolute first prerequisite is a running Kubernetes cluster (Option B) to host the services. Secondly, Run:ai utilizes Helm, the package manager for Kubernetes, to manage its complex installation charts, deployments, and service configurations. Without Helm installed on the administrative machine (Option A), the installation scripts will fail to execute. While having GPUs (Option C) is the ultimate goal for the worker nodes, the control plane itself can be installed on a cluster before all GPU hardware is physically present. Disabling NTP (Option D) is never recommended; in fact, accurate time synchronization is vital for the TLS certificates and logging used by Run:ai and Kubernetes.
Question 3
What is the primary purpose of performing a NeMo burn-in on a new AI infrastructure?
Correct Answer: B
Explanation:
The primary purpose of a NeMo burn-in is to stress test the hardware and software stack using representative NeMo workloads before releasing the AI infrastructure to production. NeMo workloads can exercise GPU compute, GPU memory, CUDA libraries, NCCL communication, storage access, checkpointing, container runtime, scheduler integration, and distributed training behavior. This makes NeMo burn-in more realistic than simply checking that GPUs are visible or that a small synthetic benchmark runs successfully. The goal is not to tune hyperparameters for model accuracy, because burn-in validates infrastructure reliability rather than model quality. It is also not mainly about ensuring all GPUs run at identical clock speeds; clock behavior can vary based on power, thermals, workload, and GPU boost behavior. What matters is that the workload runs reliably, without stalls, NCCL failures, GPU Xid errors, storage bottlenecks, memory faults, or unstable performance. In NVIDIA AI infrastructure validation, representative workload burn-in bridges the gap between low-level diagnostics and real production training, helping detect issues that synthetic tests alone may miss.
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