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NCP-AIO FAQs
It validates skills in operating, monitoring, and troubleshooting NVIDIA AI infrastructure. This certification targets professionals working in MLOps, DevOps, and system engineering roles.
The NCP-AIO exam covers several domains that reflect the daily responsibilities of AI operations professionals. Major topics include:
→ NVIDIA Base Command Manager: configuration, cluster setup, and troubleshooting.
→ Slurm cluster administration: workload scheduling, node management, and job troubleshooting.
→ Kubernetes cluster administration: deploying and managing containerized AI workloads.
→ System monitoring and performance optimization: using NVIDIA and third-party tools to identify issues and optimize throughput.
→ General troubleshooting: handling common data center problems involving compute, networking, and storage in AI environments.
The NCP-AIO exam is ideal for IT professionals, AI operations engineers, and system administrators who manage NVIDIA GPU-powered AI infrastructure. It’s also suitable for those looking to validate their skills in deploying, monitoring, and optimizing AI workloads in enterprise or data center environments.
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| NCP-AIN - NVIDIA-Certified Professional AI Networking | Buy Now |
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NVIDIA AI Operations Questions and Answers
You are managing multiple edge AI deployments using NVIDIA Fleet Command. You need to ensure that each AI application running on the same GPU is isolated from others to prevent interference.
Which feature of Fleet Command should you use to achieve this?
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?
An administrator needs to submit a script named “my_script.sh” to Slurm and specify a custom output file named “output.txt” for storing the job's standard output and error.
Which ‘sbatch’ option should be used?
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