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| Topic | Details |
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| Topic 1 | - Troubleshooting and Optimization: NVIThis section of the exam measures the skills of AI infrastructure engineers and focuses on diagnosing and resolving technical issues that arise in advanced AI systems. Topics include troubleshooting Docker, the Fabric Manager service for NVIDIA NVlink and NVSwitch systems, Base Command Manager, and Magnum IO components. Candidates must also demonstrate the ability to identify and solve storage performance issues, ensuring optimized performance across AI workloads.
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| Topic 2 | - Installation and Deployment: This section of the exam measures the skills of system administrators and addresses core practices for installing and deploying infrastructure. Candidates are tested on installing and configuring Base Command Manager, initializing Kubernetes on NVIDIA hosts, and deploying containers from NVIDIA NGC as well as cloud VMI containers. The section also covers understanding storage requirements in AI data centers and deploying DOCA services on DPU Arm processors, ensuring robust setup of AI-driven environments.
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| Topic 3 | - Administration: This section of the exam measures the skills of system administrators and covers essential tasks in managing AI workloads within data centers. Candidates are expected to understand fleet command, Slurm cluster management, and overall data center architecture specific to AI environments. It also includes knowledge of Base Command Manager (BCM), cluster provisioning, Run.ai administration, and configuration of Multi-Instance GPU (MIG) for both AI and high-performance computing applications.
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| Topic 4 | - Workload Management: This section of the exam measures the skills of AI infrastructure engineers and focuses on managing workloads effectively in AI environments. It evaluates the ability to administer Kubernetes clusters, maintain workload efficiency, and apply system management tools to troubleshoot operational issues. Emphasis is placed on ensuring that workloads run smoothly across different environments in alignment with NVIDIA technologies.
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NVIDIA AI Operations Sample Questions (Q44-Q49):
NEW QUESTION # 44
You are troubleshooting a cluster with NVIDIA NVLink and NVSwitch. The fabric manager service ('nvsm') appears to be running, but the NVLink topology is not being discovered correctly. What is the FIRST step you should take to isolate the issue?
- A. Immediately restart all GPUs in the system.
- B. Check the '/var/log/nvsm/nvsm.log' file for any error messages or warnings.
- C. Check the system's hardware for physical damage.
- D. Increase the logging level of 'nvsm' to DEBUG and restart the service.
- E. Reinstall the NVIDIA drivers.
Answer: B
Explanation:
Checking the 'nvsm.log' file is the first and most logical step. Log files often contain valuable clues about errors or warnings related to the service's operation. Debug logging can be helpful, but it's best to start with the default logging level before increasing verbosity as high verbosity can make logs harder to parse. Other steps are more intrusive and should be done after reviewing the logs.
NEW QUESTION # 45
An administrator is troubleshooting issues with NVIDIA GPUDirect storage and must ensure optimal data transfer performance.
What step should be taken first?
- A. Upgrade the CPU to a higher clock speed.
- B. Increase the GPU's core clock frequency.
- C. Install additional GPU memory (VRAM).
- D. Check for compatible RDMA-capable network hardware and configurations.
Answer: D
Explanation:
GPUDirect Storage performance relies heavily on RDMA-capable network hardware and proper configuration to enable direct memory access between storage and GPUs, bypassing CPU involvement for faster data transfers. Therefore, the first troubleshooting step should be to verify that RDMA-capable hardware is present and correctly configured. Adjusting GPU clocks, CPU speed, or GPU memory does not address the fundamental networking requirement for GPUDirect Storage.
NEW QUESTION # 46
You're deploying an AI inference application using NVIDIA Triton Inference Server in a Kubernetes cluster. Which of the following storage options is MOST suitable for storing the trained models, considering scalability and access speed?
- A. Local SSD drives directly attached to the Kubernetes nodes.
- B. Kubernetes EmptyDir volume.
- C. Kubernetes PersistentVolume backed by a cloud object storage service (e.g., AWS S3, Google Cloud Storage).
- D. HostPath volume on each node.
- E. Network File System (NFS) share mounted directly to each pod.
Answer: C
Explanation:
Using a cloud object storage service via PersistentVolume provides scalability, durability, and accessibility across the Kubernetes cluster. NFS can be a bottleneck, HostPath isn't portable, and EmptyDir is ephemeral. Local SSDs can be fast, but more difficult to manage and scale within Kubernetes for a shared model repository.
NEW QUESTION # 47
Consider the following scenario: You have a DOCA application running on a BlueField-2 DPU that performs deep packet inspection (DPI) using the DOCA DPI service. The application needs to identify specific patterns within the network traffic. Which of the following methods can be used to define the patterns for DPI?
- A. Using predefined signature databases provided by NVIDIA.
- B. Using custom C code to define matching logic.
- C. Using YAML files to define the patterns and their associated actions.
- D. Using eBPF programs to define custom pattern matching logic.
- E. Using regular expressions (RegEx) to define the patterns.
Answer: C,E
Explanation:
The doca DPI service patterns can be defined through regular expression and YAML files. Predefined signature database may exist , but that is not the primary method of definition, eBPF and Custom C are not the mechanism supported directly via DPI service.
NEW QUESTION # 48
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. Drain the compute node using scontrol update.
- D. Set the node state to down in Slurm before completing maintenance.
Answer: C
Explanation:
Before performing maintenance on a compute node in Slurm, the best practice is to drain the node to prevent new jobs from being scheduled while allowing current jobs to finish. This is done using the scontrol update NodeName=<nodename> State=Drain command 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.
NEW QUESTION # 49
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