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NEW QUESTION # 34
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)
- A. NTP is disabled to simplify time synchronization.
- B. Ensure Kubernetes is running on the cluster.
- C. All cluster nodes have NVIDIA GPUs installed.
- D. Helm is installed on the installer machine.
Answer: B,D
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 arunning Kubernetes cluster(Option B) to host the services. Secondly, Run:ai utilizesHelm, 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, thecontrol planeitself 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.
NEW QUESTION # 35
You are setting up a new AI inference server in a colocation facility. The server will be connected to a 100GbE switch managed by the facility. You have the option to use either a single-mode fiber connection with an LR4 transceiver or a multi-mode fiber connection with an SR4 transceiver. The distance between your server and the switch is approximately 75 meters. Considering cost, signal quality, and future scalability, which option is MOST suitable?
- A. SR4 transceiver with multi-mode fiber, as it is typically more cost-effective for shorter distances.
- B. LR4 transceiver with single-mode fiber, as it provides better power efficiency.
- C. Either option is equally suitable; the choice is arbitrary.
- D. AOC cable, as it provides better future scalabilty than any other type of connection
- E. LR4 transceiver with single-mode fiber, as it provides better signal quality over distance.
Answer: A
Explanation:
For a distance of 75 meters, an SR4 transceiver with multi-mode fiber is the most suitable option. It is typically more cost-effective than LR4 transceivers and provides adequate signal quality for this distance. LR4 transceivers are designed for longer distances and are more expensive. While AOCs are convenient, SR4 with multimode fiber is more cost effective. Cost-effectiveness is the defining factor between them.
NEW QUESTION # 36
Which of the following is a primary benefit of using a CLOS network topology (e.g., Spine-Leaf) in a data center?
- A. Reduced capital expenditure (CAPEX)
- B. Enhanced security
- C. Simplified network management
- D. Increased network diameter
- E. Improved scalability and bandwidth utilization
Answer: E
Explanation:
CLOS networks like Spine-Leaf provide excellent scalability due to their non-blocking architecture, allowing for increased bandwidth utilization and easy expansion. CAPEX might be higher due to more switches. The network diameter can be larger compared to traditional topologies. While CLOS networks can be managed effectively, the management complexity can be higher. Security benefits are not a primary characteristic of the CLOS topology itself.
NEW QUESTION # 37
An administrator installs NVIDIA GPU drivers on a DGX H100 system with UEFI Secure Boot enabled.
After reboot, the drivers fail to load. What is the first action to resolve this issue?
- A. Delete /etc/X11/xorg.conf to force driver reconfiguration.
- B. Reinstall drivers using apt-get install nvidia-driver-550 without rebooting.
- C. Disable Secure Boot permanently in BIOS/UEFI settings.
- D. Enroll the Machine Owner Key (MOK) during system reboot and enter the recorded password.
Answer: D
Explanation:
UEFI Secure Boot is a security standard that ensures only digitally signed code is allowed to execute during the boot process. Since NVIDIA GPU drivers include kernel modules (nvidia.ko), they must be signed by a key trusted by the system's firmware. When drivers are installed on a DGX system with Secure Boot active, the installation process generates a uniqueMachine Owner Key (MOK). However, the Linux kernel will not trust this key until the user manually authenticates it at the "Shim" level before the OS loads. Upon the first reboot after installation, the system enters the "MOK Management" blue screen. The administrator must select
"Enroll MOK" and enter the temporary password created during the driver installation. Failing to do this results in the kernel rejecting the nvidia module, leading to an "Unable to determine the device handle for GPU" error in nvidia-smi. Disabling Secure Boot (Option A) would resolve the symptom but violates the security posture of the AI infrastructure.
NEW QUESTION # 38
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?
- A. Leave the BMC port disconnected until after the operating system is fully configured and in production.
- B. Enable remote access to the BMC over the internet using the default admin credentials for initial troubleshooting.
- C. Connect the BMC port to a dedicated and firewalled network and change the default admin credentials.
- D. Connect the BMC port directly to the production network and retain default admin credentials for convenience.
Answer: C
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 adedicated 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.
NEW QUESTION # 39
A BlueField-3 DPUis configured to run both control plane and data plane functions. After a recent software update, you notice that the data plane performance has significantly degraded, but the control plane remains responsive. What is the MOST likely cause, assuming the update didn't introduce any code bugs, and what is the BEST approach to diagnose this issue?
- A. Firmware corruption; Re-flash the BlueField DPIJ with the latest firmware image.
- B. Resource contention; Use 'perf or 'bpftrace' to profile the data plane processes and identify resource bottlenecks (CPU, memory, cache).
- C. Driver incompatibility; Downgrade the Mellanox OFED drivers to the previous version.
- D. Power throttling; Check the DPU's power consumption and thermal status via the BMC.
- E. Network misconfiguration; Verify the MTU and QOS settings on the network interfaces.
Answer: B
Explanation:
Resource contention is the MOST likely cause, assuming no code bugs. The update may have increased the resource demands of either the control or data plane, leading to contention. Profiling the data plane processes with 'perf or 'bpftrace' helps pinpoint the bottlenecks. Downgrading drivers or reflashing firmware are more drastic steps to take after confirming resource contention isn't the issue.
NEW QUESTION # 40
You are deploying an NVIDIA GPU-accelerated application in a virtualized environment using vGPU. How does vGPU technology impact power and cooling considerations compared to a bare-metal deployment, and what specific monitoring metrics become crucial?
- A. vGPU deployments can lead to higher overall power consumption and concentrated heat generation on the host server due to resource consolidation. Monitoring metrics like GPU utilization per VM, vGPU frame rate, and host server thermal headroom become crucial.
- B. vGPU deployments have no significant impact on power and cooling requirements compared to bare-metal. Standard GPU temperature and power draw metrics are sufficient.
- C. vGPU deployments typically require less power and cooling than bare-metal, as resources are shared. The host server's overall power consumption becomes the primary monitoring metric.
- D. vGPU deployments require specialized cooling solutions that are not needed for bare metal setups.
- E. vGPU deployments eliminate the need for GPU monitoring, as the virtualization layer handles all power and cooling management.
Answer: A
Explanation:
vGPU deployments can increase overall power consumption and heat generation because multiple virtual machines share physical GPUs, concentrating the load on the host server. Monitoring GPU utilization per VM, vGPU frame rate, and host server thermal headroom becomes crucial to ensure optimal performance and prevent overheating. Host server thermal headroom refers to the difference between the maximum allowed temperature and the current operating temperature.
NEW QUESTION # 41
After upgrading the network card drivers on your A1 inference server, you experience intermittent network connectivity issues, including packet loss and high latency. You've verified that the physical connections are secure. Which of the following steps would be most effective in troubleshooting this issue?
- A. Update the server's BIOS.
- B. Run network diagnostic tools like 'ping', 'traceroute', and 'iperf3' to assess the network performance.
- C. Roll back the network card drivers to the previous version.
- D. Check the system logs for error messages related to the network card or driver.
- E. Reinstall the operating system.
Answer: B,C,D
Explanation:
Rolling back drivers is a quick way to revert to a known working state. Checking system logs will provide valuable information about driver errors or network issues. Network diagnostic tools will quantify the network performance and help isolate the problem. Reinstalling the OS is drastic and should be a last resort. Updating the BIOS is unlikely to resolve driver-related network issues unless specifically recommended for the network card.
NEW QUESTION # 42
You encounter a situation where a container running with GPU support is experiencing significant performance degradation compared to running the same application directly on the host. You have already verified that the NVIDIA drivers are correctly installed and the NVIDIA Container Toolkit is properly configured. Which of the following could be contributing factors to this performance difference?
(Select all that apply)
- A. The '-ipc=host' flag is not used when running the container, causing inter-process communication overhead.
- B. CPU pinning or NIJMA affinity is not properly configured for the container, leading to inefficient memory access.
- C. Insufficient bandwidth between CPU and GPU
- D. The container is using a significantly older version of the CUDA runtime compared to the host.
- E. The kernel version within the container is significantly different from the host kernel, leading to driver compatibility issues.
Answer: B,D
Explanation:
Using an older CUDA runtime within the container (A) can lead to performance degradation due to missing optimizations or compatibility issues with the application. Improper CPU pinning and NUMA affinity (B) can cause the container to access memory inefficiently, especially in multi-socket systems. '--ipc=host' (C) can improve performance in some cases by sharing the host's IPC namespace, but it's not always necessary and can have security implications. Kernel version differences (D) are generally handled by the NVIDIA Container Toolkit, which ensures compatibility. Insufficient bandwidth between CPU and GPU (E) might be caused by hardware issue.
NEW QUESTION # 43
You are installing four NVIDIAAIOO GPUs into a server designed for AI training. The server motherboard has multiple PCIe Gen4 x16 slots. However, the server's power supply unit (PSU) only has three 8-pin PCIe power connectors available. What is the BEST course of action to ensure all GPUs receive adequate power?
- A. Install only three GPUs and leave the fourth unpowered.
- B. Use a PCIe power splitter cable on one of the 8-pin connectors to power two GPUs.
- C. Underclock the GPUs significantly to reduce their power consumption below the available PSU capacity.
- D. Replace the existing PSU with a higher wattage PSU that has at least four 8-pin PCIe power connectors.
- E. Connect the GPUs using the motherboard's internal SATA power connectors.
Answer: D
Explanation:
Using a splitter can overload the original connector and cause instability or damage. Leaving a GPU unpowered defeats the purpose. SATA connectors are not designed for the high power requirements of GPUs. Underclocking might work, but is not a reliable long-term solution. Replacing the PSU is the safest and most reliable solution.
NEW QUESTION # 44
During BCM cluster setup, an engineer must configure bonded network interfaces on DGX nodes for high availability. Which cmsh command sequence properly configures a bond0 interface with two physical NICs?
- A. device use dgx001 ; interfaces add bond bond0 ; append interfaces enp225s0f1np1 enp97s0f1np1 ; set mode 1 ; set network internalnet
- B. device use dgx001 ; interfaces delete enp225s0f1np1 ; interfaces delete enp97s0f1np1
- C. device use dgx001 ; interfaces set enp225s0f1np1 network internalnet ; interfaces set enp97s0f1np1 network internalnet
- D. device use dgx001 ; interfaces add vlan vlan100 ; set parent bond0 ; set mode 1 ; set network internalnet
Answer: A
Explanation:
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.
NEW QUESTION # 45
You are developing a distributed deep learning application that uses multiple GPUs across several Docker containers running on different physical servers. How do you ensure that each container can access and utilize the GPUs on its respective host?
- A. Create a custom Docker network and configure each container to use the network's gateway as the default GPU device.
- B. Ensure the NVIDIA Container Toolkit is installed and configured on each host machine, and use a container orchestration platform like Kubernetes to manage the deployment and GPU assignment.
- C. Manually configure each container to use the 'CUDA VISIBLE DEVICES' environment variable to specify the GPUs it should use on its respective host.
- D. Install the same version of NVIDIA drivers on all host machines and configure a network file system (NFS) to share the CUDA libraries between the containers.
- E. Use Docker Swarm and specify GPU resource constraints in the 'docker-compose.yml' file to allocate GPUs to each service.
Answer: B
Explanation:
The most robust solution for distributed GPU utilization is to leverage a container orchestration platform like Kubernetes (B) along with the NVIDIA Container Toolkit. Kubernetes handles scheduling, resource allocation (including GPUs), and networking across multiple nodes.
The NVIDIA Container Toolkit ensures that each container can access the GPUs on its host. While (C) is useful, it's not sufficient for multi-server deployments. Docker Swarm (D) can work but lacks the sophisticated GPU scheduling capabilities of Kubernetes. NFS sharing (A) is unnecessary and can introduce performance bottlenecks. A custom Docker network (E) doesn't directly address GPU access.
NEW QUESTION # 46
You're deploying BlueField OS to an Arm-based SmartNIC. After flashing the image, the system fails to boot and you observe a kernel panic related to device tree loading. Which of the following is the most likely cause?
- A. The secure boot configuration is incorrectly set up. Disabling secure boot in the BIOS or bootloader might resolve the issue.
- B. The BlueField OS image is corrupted. A fresh download and re-flash should resolve the problem.
- C. Incorrect bootloader configuration (e.g., incorrect bootargs). The bootloader might not be pointing to the correct device tree blob (dtb) or root filesystem.
- D. Insufficient memory allocated to the initrd image. This can lead to failures during initial system setup.
- E. The flashed image is not intended for your specific BlueField card revision. Ensure that image corresponds to hardware version.
Answer: C
Explanation:
A device tree blob (DTB) describes the hardware to the kernel. If the bootloader doesn't point to the correct DTB or its boot arguments are incorrect, the kernel cannot properly initialize, leading to a panic. The other options could contribute to boot problems but a device tree problem commonly manifests as a kernel panic during initialization.
NEW QUESTION # 47
During HPL execution on a DGX cluster, the benchmark fails with "not enough memory" errors despite sufficient physical RAM. Which HPL.dat parameter adjustment is most effective?
- A. Reduce the problem size while maintaining the same block size.
- B. Disable double-buffering via BCAST parameter.
- C. Set PMAP to 1 to enable process mapping.
- D. Increase block size to 6144 to maximize GPU utilization.
Answer: A
Explanation:
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 theHBM (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.
NEW QUESTION # 48
An A1 inferencing server, using NVIDIA Triton Inference Server, experiences intermittent crashes under peak load. The logs reveal CUDA out-of-memory errors (00M) despite sufficient system RAM. You suspect a GPU memory leak within one of the models. Which strategy BEST addresses this issue?
- A. Increase the system RAM to accommodate the growing memory footprint.
- B. Upgrade the GPUs to models with larger memory capacity.
- C. Disable other models running on the same GPU to free up memory.
- D. Reduce the batch size and concurrency of the offending model in the Triton configuration.
- E. Implement CUDA memory pooling within the Triton Inference Server configuration to reuse memory allocations efficiently.
Answer: D,E
Explanation:
Options B and C directly address the 00M issue. CUDA memory pooling enables efficient reuse of GPU memory, minimizing allocations and deallocations. Reducing batch size and concurrency decreases the memory footprint of the model, alleviating the pressure on GPU memory. While upgrading GPUs (D) is a solution, it is more costly than optimizing the current configuration. Increasing system RAM (A) does not solve GPU memory issues. Disabling other models (E) reduces load but doesn't address the core problem of the memory leak in the first place.
NEW QUESTION # 49
You observe the following output from 'nvidia-smi' on a server running a large Ai training job:
What does the 'ClocksThrottleReasons.ThermalSlowdown: Active' indicate, and what immediate action should you take?
- A. The ambient temperature in the server room is too low, causing the GPU to underperform.
- B. The GPU's power draw is exceeding its thermal design power (TDP). Reconfigure the power supply.
- C. The GPU is operating at its maximum power limit, and no action is needed.
- D. The GPU driver is outdated and needs to be updated to improve thermal management.
- E. The GPU's clock speeds are being reduced to prevent overheating. Investigate the cooling system and reduce the GPU workload.
Answer: E
Explanation:
'ClocksThrottleReasons.ThermalSlowdown: Active means the GPU is throttling its clock speeds because it's overheating. The immediate action is to investigate the cooling system (fans, liquid cooling, airflow) and potentially reduce the GPU workload to lower its temperature.
NEW QUESTION # 50
You are managing a cluster of servers running Docker and NVIDIA GPUs. You want to monitor the GPU utilization of all Docker containers running on the cluster in real-time. Which tools or techniques could you use to achieve this?
- A. Implement a custom script that uses the NVIDIA Management Library (NVML) to query GPU utilization from each container.
- B. Use 'docker statS command on each host and look for the GPIJ utilization metrics.
- C. Utilize NVIDIA's NGC cloud monitoring services to automatically collect and visualize GPU metrics from all nodes.
- D. Integrate the DCGM exporter with Prometheus and Grafana to visualize GPU metrics from all nodes in the cluster.
- E. Use 'nvidia-smi' on each host and parse the output to extract GPIJ utilization for each container.
Answer: A,D
Explanation:
DCGM exporter integrated with Prometheus and Grafana is a robust solution for real-time monitoring of GPU metrics across a cluster (B). DCGM provides detailed GPU metrics, and Prometheus/Grafana offers excellent visualization capabilities. NVML (D) is a low-level API that allows you to directly query GPU information, providing flexibility for custom monitoring solutions. While 'nvidia-smi' (A) can be used, it's not ideal for cluster-wide monitoring. 'docker stats' (C) does not provide GPU utilization metrics directly. NGC (E) offers a container registry, but not built-in cluster-wide GPU monitoring.
NEW QUESTION # 51
You have a GPU-intensive application that requires the latest features of CUDA 12. However, your host system's NVIDIA driver is only compatible with CUDA 11.8. What steps can you take to enable your application to use CUDA 12 within a Docker container, without upgrading the host driver?
- A. Use a Docker image based on 'nvidia/cuda:12.0-base-ubuntu20.04'. The NVIDIA Container Toolkit will automatically handle the driver compatibility between the host and the container.
- B. Upgrade the NVIDIA driver on the host system to the latest version compatible with CUDA 12.
- C. Mount the CUDA 12 libraries from a separate Docker volume into the container and configure the accordingly.
- D. Install CUDA 12 inside the Docker container and set the 'CUDA DRIVER VERSION' environment variable to match the host driver version.
- E. Downgrade the application to use CUDA 11.8 to match the host's driver version.
Answer: A
Explanation:
The NVIDIA Container Toolkit enables compatibility between the host driver and the CUDA version within the container (B). Using a Docker image based on will allow your application to leverage CUDA 12 features. Upgrading the host driver (A) is an option but not necessary and may introduce other compatibility issues. Setting (C) is not a standard or reliable approach. Mounting CUDA libraries from a volume (D) is complex and might not resolve driver version mismatches. Downgrading the application (E) avoids the problem but sacrifices access to CUDA 12 features. Because NVIDIA ensures a degree of backwards compatibility, the newer toolkit in the container can often work with an older driver on the host.
NEW QUESTION # 52
You are tasked with replacing a redundant power supply unit (PSU) in a GPU server. The server has two 2000W PSUs. One PSU has failed, but the server is still running. Which of the following actions is the safest and most efficient way to replace the faulty PSU?
- A. Document the failure and wait until the remaining PSU fails before taking action.
- B. Hot-swap the faulty PSU with a new one while the server is running.
- C. Replace the faulty PSU, then reboot the server to ensure the new PSU is working.
- D. Wait for a scheduled maintenance window to power down the server and replace the PSU.
- E. Immediately power down the server and replace the faulty PSIJ.
Answer: B
Explanation:
Hot-swapping is the designed method for replacing redundant PSUs in many server systems. This allows the server to remain operational while the faulty PSU is replaced, minimizing downtime. Powering down the server immediately is unnecessary and causes downtime. Waiting for a maintenance window or until the remaining PSU fails increases the risk of a complete server outage.
NEW QUESTION # 53
After deploying BlueField OS, you notice that the network interfaces are not automatically configured with IP addresses. Which of the following actions would be the MOST appropriate first step to troubleshoot this issue?
- A. Restart the networking service using 'systemctl restart networking'.
- B. Check the DHCP client configuration to ensure it is enabled and properly configured to request IP addresses. Examine the logs for any errors.
- C. Manually assign static IP addresses to the interfaces using the 'ifconfig' command.
- D. Reinstall the Mellanox OFED drivers. A corrupted driver installation could cause network configuration issues.
- E. Re-flash the Bluefield OS image
Answer: B
Explanation:
In most modern systems, network interfaces are automatically configured using DHCP. Therefore, the first step is to check if the DHCP client is enabled and configured correctly. If DHCP fails, then other troubleshooting steps, such as static IP assignment or driver reinstallation, can be considered.
NEW QUESTION # 54
Which of the following are key benefits of using NVIDIA NVLink Switch in a multi-GPU server setup for AI and deep learning workloads?
- A. Increased GPU-to-GPIJ communication bandwidth.
- B. Enhanced security features compared to PCle based interconnections.
- C. Simplified GPU resource management.
- D. Reduced latency in inter-GPU data transfers.
- E. Support for larger GPU memory pools than a single server can physically accommodate.
Answer: A,D,E
Explanation:
NVLink provides significantly higher bandwidth and lower latency compared to PCle, enabling faster communication between GPUs. NVLink switches allow for pooling of GPU memory across multiple servers, enabling training of larger models that wouldn't fit on a single server's GPU memory. While it enhances infrastructure capabilities, it doesn't inherently simplify GPU resource management or directly provide enhanced security compared to PCle regarding the data transfer. Management features might exist on top of the NVLink infrastructure.
NEW QUESTION # 55
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?
- A. iblinkinfo
- B. nvidia-smi
- C. lspci | grep NVIDIA
- D. nvidia-gpu-status
Answer: B
Explanation:
The nvidia-smi (NVIDIA System Management Interface) utility is the primary tool for monitoring and managing the state of NVIDIA GPUs. When a node exhibits "jitter" or lower performance compared to its peers in a cluster, nvidia-smi provides the necessary granular data to identify the bottleneck. It reports critical metrics such as GPU utilization percentages, memory usage, and, most importantly, "Clocks Throttle Reasons." If a GPU is running slower due to power capping, thermal issues, or a hardware error (like an uncorrectable ECC error), nvidia-smi will display this state immediately. While lspci (Option A) can confirm if the GPU is physically visible on the PCIe bus, it cannot provide any telemetry regarding its operational performance or health. iblinkinfo (Option D) is a network-level tool that only monitors InfiniBand link states.
In an AI infrastructure context, nvidia-smi is the first-line diagnostic to determine if a GPU is "healthy" and operating at its intended clock speeds.
NEW QUESTION # 56
A System Administrator needs to change the scheduling behavior of a single GPU to use a fixed share scheduler. What command achieves this?
- A. esxcli system module parameters set -m nvidia -p
- B. nvidia-smi -i 0 -mig 1
- C. esxcli -i 0 -mig 18
- D. mlxconfig -d /dev/mst/mt4123_pciconf0 set LINK_TYPE_P1 =2
Answer: B
Explanation:
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.
NEW QUESTION # 57
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