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質問 # 34
A Slurm user needs to display real-time information about the running processes and resource usage of a Slurm job.
Which command should be used?
- A. sinfo -j <jobid>
- B. sstat -j <job(.step)>
- C. scontrol show job <jobid>
- D. smap -j <jobid>
正解：B
解説：
The Slurm command sstat is designed to provide real-time statistics about running jobs, including process-level details and resource usage such as CPU, memory, and GPU utilization. Using sstat
-j <jobid> or sstat -j <jobid.step> allows monitoring of active job resource consumption.
質問 # 35
Consider an HPC application heavily reliant on CODA. You plan to leverage MIG to optimize GPU resource allocation within your cluster.
Which configuration approach would BEST ensure the HPC application benefits from high GPU compute capability while coexisting with other workloads?
- A. Create a single, large MIG instance dedicated solely to the HPC application, maximizing its compute capacity.
- B. Configure all MIG instances with equal memory and compute allocation to provide a fair distribution of resources.
- C. Create multiple small MIG instances and distribute the HPC workload across them.
- D. Disable MIG and allow the HPC application to utilize the entire GPU for maximum performance.
- E. Create MIG instances tailored to the HPC application's specific memory and compute needs, allocating the necessary resources without over-provisioning. Utilize the remaining resources for other workloads.
正解：E
解説：
Tailoring MIG instances to the HPC application's specific requirements ensures efficient resource allocation and allows other workloads to utilize the remaining GPU capacity. D is not ideal for concurrent workloads. A and E don't account for specific workload requirements.
質問 # 36
Explanation:
You are running a Docker container that utilizes NVIDIA GPUs for deep learning inference. The application inside the container fails to detect the GPUs. You've verified that NVIDIA drivers are installed on the host. What is the MOST likely cause and how do you fix it?
- A. The '"gpus all' flag was not used when running the container. Re-run the container with 'docker run -gpus all ...w.
- B. The CUDA version in the container does not match the driver version on the host. Ensure the container uses a CUDA version compatible with the host driver.
- C. The host's firewall is blocking communication between the container and the NVIDIA drivers. Disable the firewall or create an exception for the container network.
- D. The NVIDIA Container Toolkit is not installed or configured correctly on the host machine. Install and configure the toolkit following NVIDIA's official documentation.
- E. The Docker daemon is not configured to use the NVIDIA runtime. Modify the '/etc/docker/daemon.json' file to include "'default-runtime": "nvidia"' and restart the Docker daemon.
正解：A、D
解説：
You are running a Docker container that utilizes NVIDIA GPUs for deep learning inference. The application inside the container fails to detect the GPUs. You've verified that NVIDIA drivers are installed on the host. What is the MOST likely cause and how do you fix it?
質問 # 37
You are deploying a containerized AI application from NGC on a cluster with multiple GPU nodes. You want to ensure that the application is distributed across multiple GPUs and nodes for maximum performance. What strategies can you employ to achieve this?
- A. Use a single container with multi-GPU support and configure it to utilize all available GPUs on the cluster.
- B. Utilize a distributed training framework like Horovod or DeepSpeed to distribute the workload across multiple GPUs and nodes.
- C. Employ a message queue system like RabbitMQ or Kafka to distribute data to the containers running on different nodes.
- D. Deploy multiple replicas of the container, each configured to use a specific subset of GPUs on different nodes.
- E. Configure Kubernetes resource quotas to limit the number of GPUs available to each container.
正解：B、C、D
解説：
B, C, and E are correct. Deploying multiple container replicas allows for distribution across nodes. Distributed training frameworks manage workload distribution. A message queue facilitates data distribution to different nodes. A is incorrect as it relies on a single container handling all GPUs. D is used for resource management, not distribution.
質問 # 38
Your Kubernetes cluster hosts several AI workloads with varying GPU requirements. Some workloads require high compute performance, while others are memory-bound. You want to optimize GPU resource allocation by bin-packing workloads with complementary resource profiles onto the same nodes. How would you approach this?
- A. Configure resource quotas to limit the overall GPU usage in the cluster.
- B. Manually assign workloads to specific nodes based on their resource requirements.
- C. Employ a custom Kubernetes scheduler that considers GPU compute and memory usage when placing pods.
- D. Implement a vertical pod autoscaler (VPA) to dynamically adjust the GPU resource requests of pods based on their actual usage.
- E. Use node affinity rules to schedule all workloads onto the same set of nodes, regardless of their resource profiles.
正解：C
解説：
The correct answer is B. A custom Kubernetes scheduler allows you to implement sophisticated bin-packing algorithms that consider both GPU compute and memory usage to optimize resource allocation. This enables you to pack workloads with complementary resource profiles onto the same nodes, maximizing GPU utilization. Option A ignores resource profiles. Option C is not scalable. Option D limits overall usage but doesn't optimize placement. Option E focuses on individual pod resource adjustment, not bin-packing.
質問 # 39
You are a Solutions Architect designing a data center infrastructure for a cloud-based AI application that requires high-performance networking, storage, and security. You need to choose a software framework to program the NVIDIA BlueField DPUs that will be used in the infrastructure. The framework must support the development of custom applications and services, as well as enable tailored solutions for specific workloads. Additionally, the framework should allow for the integration of storage services such as NVMe over Fabrics (NVMe-oF) and elastic block storage.
Which framework should you choose?
- A. NVIDIA CUDA
- B. NVIDIA NSight
- C. NVIDIA DOCA
- D. NVIDIA TensorRT
正解：C
解説：
NVIDIA DOCA (Data Center Infrastructure-on-a-Chip Architecture) is the software framework designed to program NVIDIA BlueField DPUs (Data Processing Units). DOCA provides libraries, APIs, and tools to develop custom applications, enabling users to offload, accelerate, and secure data center infrastructure functions on BlueField DPUs.
DOCA supports integration with key data center services including storage protocols such as NVMe over Fabrics (NVMe-oF), elastic block storage, and network security and telemetry. It enables tailored solutions optimized for specific workloads and high-performance infrastructure demands.
質問 # 40
You are setting up a multi-tenant Run.ai cluster. Two teams, 'Team Alpha' and 'Team Beta', require access. You want to ensure 'Team Alpha' always has priority access to GPUs and cannot be starved of resources, even when 'Team Beta' submits a large number of jobs.
Which Run.ai configuration option BEST achieves this?
- A. Use node affinity rules to dedicate specific nodes to 'Team Alpha'.
- B. Implement preemption policies to allow 'Team Alpha' jobs to preempt 'Team Beta' jobs.
- C. Disable the fair-share scheduler.
- D. Set equal resource quotas for both teams.
- E. Configure 'Team Alpha' with a higher priority within the fair-share scheduler.
正解：B、E
解説：
Configuring a higher priority within the fair-share scheduler ensures 'Team Alpha' gets preferential access to resources. Additionally, implementing preemption allows 'Team Alpha' to reclaim resources from 'Team Beta' if needed. While node affinity could provide dedicated resources, it doesn't dynamically address resource contention when 'Team Alpha' needs more than its dedicated nodes. Equal quotas and disabling the scheduler do not provide priority. Note that in new run.ai setups, ACM will be configured and you configure fair-share at ACM.
質問 # 41
You are using 'nvsm' to manage NVLink across multiple nodes. You need to ensure that the 'nvsm' service is automatically started on all nodes after a system reboot. Which of the following methods is the MOST reliable way to achieve this?
- A. Create a cron job that runs at reboot to start the 'nvsm' service.
- B. Place the 'nvsm' executable in the 'letc/init.dP directory.
- C. Manually start 'nvsm' after each reboot.
- D. Use a systemd service unit file to manage the 'nvsm' service.
- E. Add the 'nvsm' command to the '/etc/rc.locar file.
正解：D
解説：
Using a systemd service unit file is the most modern and reliable way to manage services on Linux systems. Systemd provides features like dependency management, automatic restarts, and logging, making it the preferred method for ensuring that 'nvsm' is started automatically after a reboot. "rc.local', '/etc/init.d' , and cron jobs are older methods that are less reliable or less well-integrated with modern systems.
質問 # 42
You're running a large-scale distributed training job using PyTorch and notice that the data loading process is a bottleneck. Your data is stored on an object storage system. Which strategies can you employ to optimize data loading performance, especially considering the distributed nature of the training?
- A. Reduce the batch size to minimize the amount of data loaded per iteration.
- B. Use PyTorch's 'DataLoader' with a high 'num_workers' value, even if it exceeds the number of CPU cores available.
- C. Implement data caching on the local NVMe drives of each worker node to avoid repeated downloads from the object storage.
- D. Ensure data is stored in a format optimized for parallel reads (e.g., Parquet, Apache Arrow) on the object store.
- E. Use a distributed file system (e.g., Lustre, BeeGFS) as an intermediate layer between the object storage and the worker nodes.
正解：C、D、E
解説：
Data caching on NVMe drives significantly reduces the need to repeatedly fetch data from object storage. Introducing a distributed filesystem allows a central point where to access the objects. Parquet and Apache Arrow are optimized for columnar data that can be used for parallel access and loading of data from an object store.
質問 # 43
You need to configure BCM to send alerts when a GPU's temperature exceeds a critical threshold. Where would you configure this alerting policy within BCM?
- A. Within the DCGM configuration files on the GPU nodes.
- B. In the 'bcm_config.yaml' file.
- C. Through the BCM web interface, in the 'Alerting Policies' section.
- D. By creating a custom Prometheus rule and integrating it with BCM.
- E. Using the 'nvidia-smi' command-line tool to set temperature thresholds and trigger alerts.
正解：C
解説：
BCM provides a dedicated 'Alerting Policies' section in its web interface where you can define rules and thresholds for various metrics, including GPU temperature. You can configure the specific threshold, the alert severity, and the notification channels (e.g., email, Slack). Other options are either not directly supported or are more complex and less integrated.
質問 # 44
A user reports that their AI training job running on a BCM-managed cluster is experiencing slow 1/0 performance. What steps would you take to diagnose and resolve the issue, considering the potential involvement of storage?
- A. Verify that the correct storage class is being used for the persistent volumes used by the training job.
- B. Monitor the storage system's performance metrics (IOPS, latency, throughput) using the storage vendor's monitoring tools.
- C. Inspect the pod's logs for any I/O related errors or warnings.
- D. Check the network bandwidth between the compute nodes and the storage system using *iperf or similar tools.
- E. Increase the pod's CPU and memory limits to improve 1/0 performance.
正解：A、B、C、D
解説：
Network bandwidth can be a bottleneck. Storage system metrics are crucial for identifying storage-related issues. The storage class determines the underlying storage type and performance characteristics. Pod logs might contain error messages. Increasing CPU/memory won't directly solve I/O performance issues if the bottleneck is elsewhere.
質問 # 45
A user reports that they are unable to submit jobs to a specific partition. You've verified that the partition exists and is enabled. What are the possible reasons for this?
- A. The partition's state is set to INACTIVE.
- B. The user's account is not associated with the partition.
- C. The user has exceeded their QOS limit.
- D. The 'MaxNodes' parameter for the partition is set to 0.
- E. All of the above
正解：E
解説：
All the options are reasons for the user to be unable to submit jobs to a specific partition. All must be checked to solve the root problem.
質問 # 46
Which of the following correctly identifies the key components of a Kubernetes cluster and their roles?
- A. Worker nodes manage the kube-apiserver and etcd, while the control plane handles all container runtimes.
- B. The control plane consists of the kube-apiserver, etcd, kube-scheduler, and kube-controller-manager, while worker nodes run kubelet and kube-proxy.
- C. The control plane includes the kubelet and kube-proxy, and worker nodes are responsible for running etcd and the scheduler.
- D. The control plane is responsible for running all application containers, while worker nodes manage network traffic through etcd.
正解：B
解説：
Comprehensive and Detailed Explanation From Exact Extract:
In Kubernetes architecture, thecontrol planeis composed of several core components including thekube- apiserver,etcd(the cluster's key-value store),kube-scheduler, andkube-controller-manager. These manage the overall cluster state, scheduling, and orchestration of workloads. Theworker nodesare responsible for running the actual containers and include thekubelet(agent that communicates with the control plane) and kube-proxy(handles network routing for services). Other options incorrectly assign these components or roles.
質問 # 47
You are deploying a distributed AI training workload across multiple geographically separated data centers. Which network architecture would BEST minimize latency for inter-node communication?
- A. A VPN (Virtual Private Network) over the public internet.
- B. Public internet with standard TCP/IP routing.
- C. A content delivery network (CDN).
- D. A dedicated private network with DWDM (Dense Wavelength Division Multiplexing) and optimized routing.
- E. A wireless mesh network.
正解：D
解説：
For geographically distributed training, minimizing latency is paramount. A dedicated private network with DWDM and optimized routing provides the lowest latency and most predictable performance compared to the public internet, VPNs, or CDNs. DWDM maximizes the bandwidth over fiber optic cables. A CDN is designed for content delivery, not low-latency communication between training nodes.
質問 # 48
After completing the installation of a Kubernetes cluster on your NVIDIA DGX systems using BCM, how can you verify that all worker nodes are properly registered and ready?
- A. Run kubectl get pods to check if all worker pods are running as expected.
- B. Check each node manually by logging in via SSH and verifying system status with systemctl.
- C. Run kubectl get nodes to verify that all worker nodes show a status of "Ready".
正解：C
解説：
The standard method to verify that worker nodes are correctly registered and ready in a Kubernetes cluster is to run kubectl get nodes. This command lists all nodes and their statuses.
Nodes showing a status of "Ready" indicates they are properly connected and available to schedule workloads. Checking pods or manual SSH is not the direct or reliable way to verify node readiness.
質問 # 49
You are using the Run.ai CLI to monitor the status of a specific job with ID 'job-1 23'. The job is currently in a 'Pending' state. What command would you use to get detailed information about why the job is pending?
- A. runai debug job-123
- B. runai get events job-123
- C. runai describe job job-123
- D. kubectl describe pod job-123
- E. runai logs job-123
正解：C
解説：
The 'runai describe job job-123' command provides detailed information about the job, including its status, resource requests, and any events or messages that explain why the job is in a pending state. 'runai get events would show events, but 'describe' gives a more comprehensive overview. 'runai logs would only show logs once the job starts running. 'runai debug' is for interactive debugging sessions. 'kubectl describe pod' might provide some information, but it's less integrated with Run.ai's scheduling and resource management features.
質問 # 50
A system administrator notices that jobs are failing intermittently on Base Command Manager due to incorrect GPU configurations in Slurm. The administrator needs to ensure that jobs utilize GPUs correctly.
How should they troubleshoot this issue?
- A. Ensure that GPU resource limits have been correctly defined in Slurm's configuration file for each job type.
- B. Increase the number of GPUs requested in the job script to avoid using unconfigured GPUs.
- C. Verify that non-MIG GPUs are automatically configured in Slurm when detected, and adjust configurations if needed.
- D. Check if MIG (Multi-Instance GPU) mode has been enabled incorrectly and reconfigure Slurm accordingly.
正解：D
解説：
Misconfiguration related to MIG mode can cause Slurm to improperly allocate GPUs, leading to job failures. The administrator should verify whether MIG has been enabled on the GPUs and ensure that Slurm's configuration matches the hardware setup. If MIG is enabled, Slurm must be configured to recognize and schedule MIG partitions correctly to avoid resource conflicts.
質問 # 51
A system administrator needs to collect the information below:
* GPU behavior monitoring
* GPU configuration management
* GPU policy oversight
* GPU health and diagnostics
* GPU accounting and process statistics
* NVSwitch configuration and monitoring
What single tool should be used?
- A. DCGM
- B. nvidia-smi
- C. Nsight Systems
- D. CUDA Toolkit
正解：A
解説：
Comprehensive and Detailed Explanation From Exact Extract:
TheNVIDIA Data Center GPU Manager (DCGM)is the comprehensive management tool that provides all the requested functionalities: monitoring GPU behavior, managing configurations, enforcing policies, health diagnostics, process accounting, and NVSwitch monitoring. DCGM is designed for large-scale GPU management in data centers and AI clusters, providing detailed telemetry and control over NVIDIA GPUs and NVSwitches.
* nvidia-smiprovides GPU monitoring but lacks full policy and NVSwitch management.
* CUDA Toolkit is for GPU programming and development.
* Nsight Systems is focused on performance profiling and debugging.
Therefore, DCGM is the single tool that meets all the listed requirements.
質問 # 52
You are deploying AI applications at the edge and want to ensure they continue running even if one of the servers at an edge location fails.
How can you configure NVIDIA Fleet Command to achieve this?
- A. Configure Fleet Command's multi-instance GPU (MIG) to handle failover.
- B. Enable high availability for edge clusters.
- C. Use Secure NFS support for data redundancy.
- D. Set up over-the-air updates to automatically restart failed applications.
正解：B
解説：
Comprehensive and Detailed Explanation From Exact Extract:
To ensure continued operation of AI applications at the edge despite server failures, NVIDIA Fleet Command allows administrators toenable high availability (HA) for edge clusters. This HA configuration ensures redundancy and failover capabilities, so applications remain operational when an edge server goes down.
Over-the-air updates handle software patching but do not inherently provide failover. MIG manages GPU resource partitioning, not failover. Secure NFS supports storage redundancy but is not the primary solution for application failover.
質問 # 53
A Docker container running a CUDA application terminates unexpectedly with an 'out of memory' error, despite the host machine having sufficient RAM. What are the potential causes and how would you diagnose them?
- A. The Docker image is corrupted. Rebuild the Docker image from scratch.
- B. The host machine is experiencing swap thrashing. Monitor swap usage on the host machine and consider increasing RAM or optimizing the application's memory usage.
- C. The GPU is running out of memory. Monitor GPU memory usage using 'nvidia-smr within the container to identify memory leaks or excessive memory allocation.
- D. The container's memory limit is too low. Increase the container's memory limit using the -memory' flag with 'docker rum.
- E. The application is experiencing a memory leak within the container. Use memory profiling tools within the container to identify and fix the memory leak.
正解：C、D、E
解説：
The error could stem from a constrained container memory limit, GPU memory exhaustion, or a memory leak within the application itself. Each of these requires different diagnostic approaches. Container memory limits can be checked and adjusted. GPU memory usage can be monitored. Application memory leaks need specific profiling tools inside the container.
質問 # 54
You are configuring BCM for cluster provisioning. You want to automate the installation of specific software packages on each newly provisioned node. How can you achieve this?
- A. Create a Kubernetes Job that runs on each node to install the packages.
- B. Use a BCM post-provisioning script to install the packages.
- C. Include the package installation commands directly in the OS image.
- D. Specify the packages in the 'cluster.yamr file under the 'packages' section.
- E. Leverage a configuration management tool like Ansible or Chef within a BCM post-provisioning script.
正解：B、C、E
解説：
Including packages in the OS image is a direct approach. Post-provisioning scripts allow customization after the base OS is installed. Configuration management tools offer more sophisticated automation. Kubernetes Jobs are designed for workload execution, not system-level package management. BCM does not have a 'packages' section in 'cluster.yamr for direct package specification.
質問 # 55
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