NVIDIA Multi-Instance GPU (MIG) technology, introduced with the Ampere architecture (A100) and enhanced in Hopper (H100), allows a single physical GPU to be partitioned into multiple isolated instances.
To manage these instances and their scheduling behavior (such as moving from a default time-sliced scheduler to a fixed-share or " MIG " partitioned mode), the nvidia-smi utility is used. The command nvidia- smi -i 0 -mig 1 enables MIG mode on the first GPU (index 0). Once MIG is enabled, the administrator can create specific GPU instances with dedicated compute and memory resources. This " Fixed Share " approach ensures that one tenant ' s workload does not impact the performance or latency of another, which is critical for deterministic AI inference and multi-tenant cloud environments. Option A and B refer to VMware ESXi specific commands which are not the primary method for raw hardware configuration in standard AI infrastructure, and Option D is for network adapter configuration.

The correct essential steps are grouping nodes by function and validating networking on every node. In DGX cluster deployments, nodes are commonly organized by role, such as head nodes, compute nodes, login nodes, storage nodes, or management components. Cluster management tools such as NVIDIA Base Command Manager use these logical groupings or categories to apply software images, configurations, monitoring, and operational actions consistently. NVIDIA DGX SuperPOD documentation describes cluster manager concepts where devices represent components such as head nodes, physical nodes, switches, and PDUs, and older DGX SuperPOD guidance references default BCM node categories for login and compute systems. Network validation is equally important before higher-level software deployment because InfiniBand, Ethernet, management, and storage interfaces must be correctly cabled, addressed, and reachable. Installing Slurm before compute node images are correctly prepared reverses the expected foundation-first workflow. Skipping node health or storage validation is unsafe because distributed workloads depend on consistent GPU health, network reachability, and storage access. A reliable DGX cluster installation starts with structured node roles, validated networks, consistent images, and then workload orchestration and application testing.

The correct answer is ngc config set --org org-name --ace ace-name. NGC CLI uses configuration context to determine which organization, team, or entitlement scope the user is operating in. When users belong to multiple organizations, the CLI must be configured with the intended organization context so that registry access, model downloads, private containers, and enterprise software resources resolve correctly. NVIDIA documentation shows ngc config set as the command used to configure or switch NGC context, including organization and team values, and other NVIDIA installation workflows show NGC configuration prompting for organization, team, and ACE fields. Manually editing JSON configuration files is not the recommended operational method because it increases the risk of syntax errors and inconsistent authentication state. ngc registry login is not the normal command for setting persistent NGC CLI organization context. Exporting an environment variable may influence a session, but it is not the best persistent configuration method. In AI infrastructure operations, correct NGC configuration is important for pulling validated containers, GPU drivers, vGPU software, models, and enterprise assets from the correct account scope.

High-Performance Linpack (HPL) is a memory-intensive benchmark that allocates a large portion of available GPU memory to store the matrix $N$. While a server may have 2TB of physical system RAM, the " not enough memory " error usually refers to the HBM (High Bandwidth Memory) on the GPUs themselves. In a DGX H100 system, each GPU has 80GB of HBM3. If the problem size ($N$) specified in the HPL.dat file is too large, the required memory for the matrix will exceed the aggregate capacity of the GPU memory.
Reducing the problem size ($N$) while maintaining the optimal block size ($NB$) ensures that the problem fits within the GPU memory limits while still pushing the computational units to their peak performance.
Increasing the block size (Option C) would actually increase the memory footprint of certain internal buffers, potentially worsening the issue. Reducing $N$ is the standard procedure to stabilize the run during the initial tuning phase of an AI cluster bring-up.

In NVIDIA Base Command Manager (BCM), the management and storage traffic often requires redundancy via NIC bonding (Link Aggregation). The cmsh utility uses a hierarchical command structure to modify node configurations. To create a bond, the administrator must first navigate to the specific node ' s interface configuration (device use < node > ). The correct sequence involves adding a new interface of type " bond " (interfaces add bond bond0). After the bond object is created, the physical slave interfaces must be associated with it using the append interfaces command. Finally, the bonding mode (e.g., Mode 1 for Active-Backup or Mode 4 for LACP) and the logical network assignment must be defined. Option B correctly follows this logic.
Assigning networks directly to physical ports (Option C) would prevent the use of a unified bond IP, and Option A incorrectly attempts to add a VLAN before the underlying bond is established. This configuration is essential for ensuring the control plane remains reachable even if a single management cable or switch port fails.