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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 | - 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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| Topic 4 | - 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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NVIDIA AI Operations Sample Questions (Q12-Q17):
NEW QUESTION # 12
Which of the following storage technologies provides the best support for handling a large number of small files, common in AI datasets (e.g., image datasets)?
- A. Traditional block storage (e.g., SAN) with a large block size.
- B. Tape storage
- C. Parallel file system (e.g., BeeGFS) designed for high IOPS and low latency.
- D. Object storage (e.g., AWS S3) with optimized metadata management.
- E. Network Attached Storage (NAS) using NFS.
Answer: C,D
Explanation:
Object storage systems, especially those optimized for metadata management, are well-suited for storing and accessing large numbers of small files. Parallel file systems are also designed for high IOPS and low latency, making them efficient for handling numerous small file operations. Traditional block storage with a large block size can lead to wasted space. NAS over NFS can become a bottleneck. Tape is unsuitable for frequently accessing data.
NEW QUESTION # 13
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. Use the runai-adm command to directly update Kubernetes nodes without requiring kubectl.
- B. Use the CLI to manually allocate specific GPUs to individual jobs for better resource management.
- C. Install the CLI on Windows machines to take advantage of its scripting capabilities.
- D. Ensure that the Kubernetes configuration file is set up with cluster administrative rights before using the CLI.
Answer: D
Explanation:
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.
NEW QUESTION # 14
Which BCM configuration file defines the operating system image used for provisioning new nodes?
- A. image.yaml
- B. cluster.yaml
- C. bcm.conf
- D. provisioning.yaml
- E. node.yaml
Answer: B
Explanation:
The 'cluster.yamr file in BCM typically contains the definition of the operating system image, including the URL or path to the image file, along with other cluster-level configuration details like network settings and Kubernetes version.
NEW QUESTION # 15
A system administrator wants to run these two commands in Base Command Manager.
main showprofile device status apc01
What command should the system administrator use from the management node system shell?
- A. cmsh-system -c "main showprofile; device status apc01"
- B. system -c "main showprofile; device status apc01"
- C. cmsh -p "main showprofile; device status apc01"
- D. cmsh -c "main showprofile; device status apc01"
Answer: D
Explanation:
The Base Command Manager command shell (cmsh) accepts the -c flag to execute multiple commands sequentially. Using cmsh -c "main showprofile; device status apc01" runs the main showprofile followed by device status apc01 commands in one invocation, allowing scripted or batch execution from the management node shell.
NEW QUESTION # 16
Which of the following Slurm configuration options are typically modified within the "slurm.conf' file? (Select TWO)
- A. MailType
- B. JobSubmitPlugins
- C. squeue
- D. SchedulerParameters
- E. sacctmgr
Answer: B,D
Explanation:
'JobSubmitPluginS configures plugins for job submission, and 'SchedulerParameterS configures the behavior of the scheduler. These are typically defined cluster-wide in 'slurm.conf. 'squeue' and 'sacctmgr' are commands, not configuration options. 'MailType' can be configured but depends on the email plugin being setup.
NEW QUESTION # 17
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