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質問 # 20
You're managing a cluster using Kubernetes and Ceph, and your AI training jobs are experiencing storage I/O bottlenecks. You want to use Rook to manage Ceph within Kubernetes effectively. What configurations in Rook and Kubernetes would you verify to optimize storage performance for your AI workloads?
- A. Disable Ceph monitoring within Rook to reduce overhead on the cluster. pool: data').
- B. Configure Ceph placement groups (PGs) and pools to match the workload characteristics (e.g., number of objects, access patterns).
- C. Ensure that the Ceph OSDs are running on fast storage devices (e.g., NVMe SSDs) and have sufficient resources (CPU, memory).
- D. Verify that the Kubernetes pods have appropriate resource requests and limits to prevent resource contention.
- E. Modify Rook's default storage class to use the 'rbd' provisioner with optimized parameters for AI workloads (e.g., 'imageFeatures: layering'
正解：B、C、D、E
解説：
OSD performance is crucial for Ceph's overall performance. Resource requests/limits prevent pod resource starvation. Optimizing PGs and pools aligns Ceph with the workload. Configuring vrbd' provisioner with optimized parameters will help improve overall performance. Monitoring is important to debug issues, do not disable.
質問 # 21
Which of the following statements regarding the NVIDIA Device Plugin for Kubernetes are correct?
- A. It exposes GPUs as schedulable resources to Kubernetes.
- B. It automatically installs the NVIDIA drivers on the nodes.
- C. It ensures that containers have the necessary NVIDIA libraries and tools.
- D. It allows Kubernetes to be aware of the NVIDIA GPUs present on the nodes.
- E. It replaces the need for the NVIDIA Container Toolkit.
正解：A、D
解説：
The correct answers are A and C. The NVIDIA Device Plugin discovers NVIDIA GPUs on each node and advertises them as resources to the Kubernetes scheduler. It enables Kubernetes to allocate GPUs to containers. It does not install drivers (that's a separate process). It works with the NVIDIA Container Toolkit to provide the necessary libraries within the container. It does not replace the NVIDIA Container Toolkit; they work in conjunction.
質問 # 22
You are an administrator managing a large-scale Kubernetes-based GPU cluster using Run:AI.
To automate repetitive administrative tasks and efficiently manage resources across multiple nodes, which of the following is essential when using the Run:AI Administrator CLI for environments where automation or scripting is required?
- A. Ensure that the Kubernetes configuration file is set up with cluster administrative rights before using the CLI.
- B. Install the CLI on Windows machines to take advantage of its scripting capabilities.
- C. Use the runai-adm command to directly update Kubernetes nodes without requiring kubectl.
- D. Use the CLI to manually allocate specific GPUs to individual jobs for better resource management.
正解：A
解説：
Comprehensive and Detailed Explanation From Exact Extract:
When automating tasks with the Run:AI Administrator CLI, it is essential to ensure that theKubernetes configuration file (kubeconfig)is correctly set up with cluster administrative rights. This enables the CLI to interact programmatically with the Kubernetes API for managing nodes, resources, and workloads efficiently.
Without proper administrative permissions in the kubeconfig, automated operations will fail due to insufficient rights.
Manual GPU allocation is typically handled by scheduling policies rather than CLI manual assignments. The CLI does not replacekubectlcommands entirely, and installation on Windows is not a critical requirement.
The Run:AI Administrator CLI requires a Kubernetes configuration file with cluster-administrative rights in order to perform automation or scripting tasks across the cluster. Without those rights, the CLI cannot manage nodes or resources programmatically.
質問 # 23
Your Kubernetes cluster is running a mixture of AI training and inference workloads. You want to ensure that inference services have higher priority over training jobs during peak resource usage times.
How would you configure Kubernetes to prioritize inference workloads?
- A. Increase the number of replicas for inference services so they always have more resources than training jobs.
- B. Use Horizontal Pod Autoscaling (HPA) based on memory usage to scale up inference services during peak times.
- C. Set up a separate namespace for inference services and limit resource usage in other namespaces.
- D. Implement ResourceQuotas and PriorityClasses to assign higher priority and resource guarantees to inference workloads over training jobs.
正解：D
解説：
Comprehensive and Detailed Explanation From Exact Extract:
To prioritize inference workloads over training jobs in Kubernetes, administrators should configurePriorityClassesandResourceQuotas. PriorityClasses allow assigning different priority levels to pods, ensuring that during resource contention, higher-priority pods (inference services) receive resources first.
ResourceQuotas limit the resource consumption per namespace or user, controlling overall usage and reserving capacity for critical workloads. This setup effectively manages resource allocation and guarantees performance for inference jobs during peak times.
* Increasing replicas or namespaces alone does not guarantee priority during contention.
* HPA scales based on metrics but does not manage priority or resource guarantees directly.
質問 # 24
You need to do maintenance on a node. What should you do first?
- A. Set the node state to down in Slurm before completing maintenance.
- B. Disable job scheduling on all compute nodes in Slurm before completing maintenance.
- C. Set the node state to down in Slurm before completing maintenance.
- D. Drain the compute node using scontrol update.
正解：D
解説：
Comprehensive and Detailed Explanation From Exact Extract:
Before performing maintenance on a compute node in Slurm, the best practice is todrain the nodeto prevent new jobs from being scheduled while allowing current jobs to finish. This is done using thescontrol update NodeName=<nodename> State=Draincommand or equivalent. Setting the node state to down immediately may disrupt running jobs, and disabling scheduling on all nodes is unnecessarily broad. Draining ensures a controlled transition for maintenance.
質問 # 25
An organization has multiple containers and wants to view STDIN, STDOUT, and STDERR I/O streams of a specific container.
What command should be used?
- A. docker inspect CONTAINER-NAME
- B. docker stats CONTAINER-NAME
- C. docker logs CONTAINER-NAME
- D. docker top CONTAINER-NAME
正解：C
解説：
Comprehensive and Detailed Explanation From Exact Extract:
Thedocker logs CONTAINER-NAMEcommand retrieves thestandard output (STDOUT)andstandard error (STDERR)streams of a running or stopped container. It is the primary tool for inspecting container I/O logs for debugging and monitoring purposes.docker topshows running processes,docker statsshows resource usage, anddocker inspectshows metadata/configuration.
質問 # 26
You're building a new AI data center and need to select a suitable data center location. Which of the following factors are MOST important to consider? (Select TWO)
- A. Availability of skilled technical staff.
- B. Local tax incentives.
- C. Proximity to a major airport.
- D. Low real estate costs.
- E. Reliable and cost-effective power supply.
正解：A、E
解説：
Reliable and cost-effective power is crucial for operating a high-density AI data center. The availability of skilled technical staff is essential for managing and maintaining the infrastructure. While real estate costs and tax incentives are relevant, they are secondary to power and expertise. Proximity to an airport is less important. The location must be sustainable and scalable. These are very important points to take into account.
質問 # 27
You are tasked with optimizing the performance of a large-scale graph analytics application that uses NVSHMEM for distributed shared memory. The application spends a significant amount of time in remote memory accesses. Which of the following strategies would be MOST effective in reducing the overhead of these remote accesses?
- A. Reduce the size of the graph.
- B. Disable CUDA-Aware MPI support
- C. Switch to a CPU-based implementation.
- D. Use NVSHMEM collectives for bulk data transfers.
- E. Increase the number of GPUs per node.
正解：D
解説：
NVSHMEM collectives provide optimized routines for performing operations on shared memory across multiple processing elements (PEs). Using collectives for bulk data transfers, such as 'nvshmem_putmem' or 'nvshmem_getmem', is significantly more efficient than performing many individual small remote memory accesses. Increasing the number of GPUs per node might help with local computations but doesn't directly address remote access overhead. Reducing the graph size is not always feasible. A CPU-based implementation would likely be slower. Disabling CUDA-Aware MPI would degrade network communication speed, so not a good option.
質問 # 28
A user is running a large language model (LLM) training job on a multi-GPU server. The job utilizes PyTorch's 'DistributedDataParallel' (DDP). The training process seems to hang intermittently. How can you troubleshoot this issue using system management tools?
- A. Use 'perf to profile the CPU usage of the PyTorch processes.
- B. Examine the system logs C/var/log/syslog' or similar) for any error messages related to network or GPU communication.
- C. Monitor network bandwidth utilization between GPUs using 'ethtoor.
- D. Use 'nvidia-smi' to check GPU utilization and memory usage to identify potential imbalances across GPUs.
- E. Run 'iostat' to observe disk 1/0 activity, as it might be causing delays.
正解：B、C、D
解説：
DDP relies on efficient inter-GPU communication. Network bottlenecks (A) can cause hangs. GPU utilization imbalances (B) can lead to some GPUs waiting for others. System logs (C) might contain error messages indicating communication failures. While CPU profiling (D) and disk I/O monitoring (E) might be useful in other scenarios, they are less likely to be the primary cause of hangs in DDP training.
質問 # 29
You have an NVIDIAA100 GPU configured with MIG. After restarting the system, the MIG instances are no longer present. Which step is necessary to ensure MIG configurations persist after a reboot?
- A. Save the MIG configuration to the persistence database using 'nvidia-smi mig -Igip' . Also make sure you enable persistence mode.
- B. The MIG configuration is stored in the BIOS; no additional steps are necessary.
- C. Save the MIG configuration to a file using 'nvidia-smi mig -SIP and load it on system startup.
- D. Enable the 'MIG Persistence' option in the NVIDIA Control Panel.
- E. Update the NVIDIA driver after each system restart.
正解：A
解説：
MIG configurations are not persistent by default. You can use command to load and save instance placement to persistence DB (Igip). The '-Igip' option stores the configuration, and the '-elgip' option ensures it is loaded on system startup. Make sure you also enable persistence mode, so that the setting will survive a system restart.
質問 # 30
An administrator is troubleshooting issues with an NVIDIA Unified Fabric Manager Enterprise (UFM) installation and notices that the UFM server is unable to communicate with InfiniBand switches.
What step should be taken to address the issue?
- A. Disable the firewall on the UFM server to allow communication.
- B. Install additional GPUs in the UFM server to boost connectivity.
- C. Verify the subnet manager configuration on the InfiniBand switches.
- D. Reboot the UFM server to refresh network connections.
正解：C
解説：
Comprehensive and Detailed Explanation From Exact Extract:
Communication issues between UFM server and InfiniBand switches often result from misconfigured or missingsubnet manager configurationon the switches. The subnet manager controls fabric membership and routing, so verifying and correcting its setup is essential for proper UFM operation. Rebooting, adding GPUs, or disabling firewalls are less likely to resolve fabric-level communication problems.
質問 # 31
Explain the process to perform a Blue-Green deployment for an AI model serving application running on a BCM-managed Kubernetes cluster. How do you minimize downtime and ensure a smooth transition?
- A. Use a service mesh (e.g., Istio) to gradually shift traffic from the old version to the new version, monitoring metrics and performing rollbacks if necessary.
- B. Deploy the new version of the application alongside the existing version, then switch the service to point to the new version once it's ready.
- C. Update the existing deployment in place, using a rolling update strategy with a small 'maxSurge' and 'maxUnavailable' to minimize disruption.
- D. Take the existing application offline, deploy the new version, and then bring the application back online.
- E. Create a new Kubernetes namespace for the new version, deploy the application, and then migrate traffic using DNS changes.
正解：A、B
解説：
Blue-green involves deploying a parallel, identical environment (the 'blue' and 'green' versions) and switching traffic. A direct service switch after verifying the new version minimizes downtime. Service meshes provide fine-grained traffic control, enabling gradual rollouts and rollbacks. Rolling updates are more like incremental updates rather than switching. DNS migration isn't instant. Taking the app offline causes significant downtime. The service mesh can provide a safe path to Blue-Green.
質問 # 32
You are tasked with deploying a multi-tenant AI cluster using Base Command Manager (BCM). How would you best isolate tenant workloads to ensure security and resource utilization?
- A. Deploy individual VMS for each tenant's workloads, managed directly by BCM.
- B. Rely solely on user authentication and authorization for workload isolation.
- C. Create separate physical clusters for each tenant.
- D. Use Docker containers without resource limits, relying on the OS to manage resources.
- E. Utilize Kubernetes namespaces and resource quotas within a single cluster.
正解：E
解説：
Kubernetes namespaces provide a logical separation of resources within a single cluster. Resource quotas limit the amount of resources that a namespace can consume, providing isolation and preventing one tenant from monopolizing resources. Creating separate clusters is costly. User authentication/authorization isn't sufficient alone for resource isolation.
質問 # 33
A user complains that their AI training job is running very slowly. Upon investigation, you discover that the pod is scheduled onto a node with a slow network connection, causing significant delays in data transfer. How would you ensure that future similar jobs are scheduled onto nodes with faster network connections?
- A. Manually reschedule the pod onto a node with a faster network.
- B. Increase the resource requests for the pod to trigger rescheduling.
- C. Configure the kubelet to prioritize pods based on their network usage.
- D. Implement node affinity rules based on network bandwidth labels, and label the nodes appropriately.
- E. Use inter-pod affinity to force the job onto nodes already running network-intensive workloads.
正解：D
解説：
The correct answer is B. By labeling nodes with their network bandwidth capabilities (e.g., 'network-bandwidth: 100GbpS), you can then use node affinity rules in your pod specifications to ensure that jobs requiring high bandwidth are scheduled onto suitable nodes. Option A is a temporary fix. Options C and D do not address the core issue of network bandwidth. Option E would exacerbate the problem by concentrating network-intensive workloads on the same nodes.
質問 # 34
You have a Slurm cluster configured with multiple partitions, and you want to restrict a specific user group to only submit jobs to a particular partition. How can you achieve this using Slurm's Quality of Service (QOS) and Access Control features?
- A. Use the 'scontror command to set the default partition for the user group.
- B. Modify the partition configuration to include the user group in the 'AllowGroupS parameter.
- C. Edit the user's .bashrc file to include #SBATCH --partition=.
- D. Create a QOS that allows access only to the desired partition and assign that QOS to the user group using 'sacctmgr' .
- E. Configure PAM (Pluggable Authentication Modules) to restrict user access based on group membership.
正解：D
解説：
Creating a QOS that restricts access to the desired partition and associating that QOS with the user group is the most direct and controlled method. The 'sacctmgr' tool is used to manage QOS and user/group associations.
質問 # 35
You are managing a fleet of edge devices using NVIDIA Fleet Command. After deploying a new AI model, you observe that the model is consuming excessive resources on several devices, leading to performance degradation. What steps can you take within Fleet Command to address this issue?
- A. Remotely reboot the affected edge devices.
- B. Increase the overall resource allocation for the entire fleet.
- C. Roll back the deployment to the previous model version.
- D. Redeploy the same model to see if the issue resolves itself.
- E. Use Fleet Command's monitoring tools to identify the specific resources being overutilized and then reconfigure the model deployment with resource limits.
正解：E
解説：
Fleet Command's monitoring allows precise identification of resource bottlenecks. Resource limits prevent excessive consumption. Rolling back (A) is a reactive measure. Rebooting (B) is temporary. Increasing overall resources (D) is inefficient. Redeploying (E) is unlikely to solve the problem without investigation.
質問 # 36
You have a DOCA application deployed on a BlueField-3 DPU. The application utilizes multiple DOCA services, including DOCA Flow and DOCA DPI. You are experiencing performance issues, and you suspect that the bottleneck is within the DPU. How would you proceed with debugging and profiling the DOCA application to identify the source of the performance bottleneck?
- A. Using perf: Utilize the 'perf tool on the DPU to profile the DOCA application and identify the functions or code sections that are consuming the most CPU cycles.
- B. Using DOCA Tracing: Implement DOCA Tracing within the application code to record detailed information about function calls, memory allocations, and other events, then analyze the trace data to identify performance issues.
- C. Using DOCA Telemetry: Enable DOCA Telemetry to collect performance metrics for different DOCA services and identify the service with the highest latency or resource consumption.
- D. Using gdb: Attach 'gdb' to the DOCA application process and set breakpoints at strategic locations to analyze the application's behavior and identify performance bottlenecks.
- E. Analyzing DOCA logs: By analyzing DOCA Logs with debug mode, you can troubleshoot and profiling the DOCA application to identify the source of the performance bottleneck.
正解：A、B、C、D
解説：
DOCA Telemetry, 'perf, 'gdb' , and DOCA Tracing are all valuable tools for debugging and profiling DOCA applications. DOCA Telemetry provides high-level performance metrics, while 'perf and 'gdb' offer more detailed profiling and debugging capabilities. DOCA Tracing allows for recording and analyzing specific events within the application. Analyzing DOCA Logs for profiling is not a solution for debugging performance, as it only collects information about debug trace.
質問 # 37
You are trying to configure MIG (Multi-lnstance GPU) on your Run.ai cluster. You have an NVIDIAA100 GPU and want to create two MIG instances, each with 20GB of memory. Assuming the A100 has 80GB of memory, what is the CORRECT MIG profile string you would use when submitting a job to request one of these MIG instances?
- A. 4g.20gb
- B. 2g.10gb
- C. 1g.5gb
- D. 1g.10gb
- E. 2g.20gb
正解：B
解説：
The MIG profile string follows the format 'GPU instances>g.gb'. In this case, '2g.10gb' is the correct MIG profile. This is because the A100 GPU will be split into 2 instances with 10 GB memory each, not 20GB as asked in the question. Even if the A100 has 80GB of memory, MIG is not a 1-1 memory division ratio.
質問 # 38
Consider a scenario where you have the following deployment manifest for Fleet Command. What is the primary function of 'resources' section within the manifest?
- A. Specifies dependencies on external libraries or frameworks.
- B. Specifies the location of the AI model to be deployed.
- C. Defines the computational resources (e.g., CPU, GPU, memory) required by the deployment.
- D. Configures networking settings for the deployment.
- E. Determines which devices receive the deployment based on label match
正解：C
解説：
The 'resources section in a deployment manifest is primarily used to define the computational resource requirements of the deployment, ensuring that the application has access to the necessary CPU, GPU, and memory to function correctly.
質問 # 39
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