Cluster installation with Slurm, Enroot, and Pyxis: Practice Questions — Control Plane Installation and Configuration (NVIDIA-Certified Professional: AI Infrastructure)
Practice Questions: Cluster Installation with Slurm, Enroot, and Pyxis This set of multiple-choice questions is designed to help candidates prepare...
Practice Questions: Cluster Installation with Slurm, Enroot, and Pyxis
This set of multiple-choice questions is designed to help candidates prepare for the Control Plane Installation and Configuration section of the NVIDIA-Certified Professional: AI Infrastructure exam, specifically focusing on cluster installation using Slurm, Enroot, and Pyxis.
Which component is primarily responsible for managing job scheduling and resource allocation in an NVIDIA AI cluster?
- A) Enroot
- B) Slurm
- C) Pyxis
- D) nGC CLI
Correct Answer: B) Slurm
Explanation: Slurm is the workload manager that schedules jobs and manages resources across the cluster. Enroot and Pyxis facilitate container execution, while nGC CLI is a command-line interface tool.
What is the main purpose of Enroot in the context of NVIDIA AI cluster installations?
- A) Scheduling GPU jobs
- B) Providing lightweight container runtime for HPC environments
- C) Managing network configurations
- D) Installing GPU drivers
Correct Answer: B) Providing lightweight container runtime for HPC environments
Explanation: Enroot is a simple, unprivileged container runtime designed to run containers efficiently on HPC clusters, enabling GPU-accelerated workloads.
Pyxis is used in NVIDIA AI clusters primarily to:
- A) Extend Slurm to support containerized workloads
- B) Install NVIDIA GPU drivers
- C) Manage cluster networking
- D) Monitor GPU health
Correct Answer: A) Extend Slurm to support containerized workloads
Explanation: Pyxis is a Slurm plugin that integrates container runtimes like Enroot, enabling Slurm to launch containerized jobs seamlessly.
Which command is essential to verify that Slurm is properly configured to launch containerized jobs with Pyxis?
- A) scontrol show job
- B) srun --container-image
- C) nvidia-smi
- D) enroot start
Correct Answer: B) srun --container-image
Explanation: Using srun --container-image tests Slurm’s ability to launch jobs inside containers via Pyxis, confirming proper integration.
During cluster installation, which step is critical to ensure GPU resources are accessible inside containers managed by Enroot and Pyxis?
- A) Installing the NVIDIA Container Toolkit
- B) Configuring Slurm partitions
- C) Setting up the nGC CLI
- D) Installing the DOCA drivers
Correct Answer: A) Installing the NVIDIA Container Toolkit
Explanation: The NVIDIA Container Toolkit enables container runtimes like Enroot to access GPU devices inside containers, which is essential for GPU-accelerated workloads.
Which of the following best describes the relationship between Slurm, Enroot, and Pyxis in an NVIDIA AI cluster?
- A) Slurm manages containers, Enroot schedules jobs, Pyxis installs drivers
- B) Enroot schedules jobs, Pyxis manages GPUs, Slurm runs containers
- C) Slurm schedules jobs, Enroot runs containers, Pyxis integrates Slurm with Enroot
- D) Pyxis schedules jobs, Slurm runs containers, Enroot installs drivers
Correct Answer: C) Slurm schedules jobs, Enroot runs containers, Pyxis integrates Slurm with Enroot
Explanation: Slurm handles job scheduling, Enroot provides the container runtime, and Pyxis acts as the integration layer enabling Slurm to launch containerized jobs using Enroot.
Which configuration file is typically modified to enable Pyxis plugin support in Slurm?
- A) slurm.conf
- B) enroot.conf
- C) pyxis.conf
- D) nvidia-container-runtime.json
Correct Answer: A) slurm.conf
Explanation: The slurm.conf file is updated to load the Pyxis plugin so that Slurm can manage containerized workloads.
What is the recommended way to test GPU access inside a container launched via Slurm with Pyxis and Enroot?
- A) Run nvidia-smi inside the container
- B) Check Slurm job logs
- C) Use enroot list
- D) Run docker ps
Correct Answer: A) Run nvidia-smi inside the container
Explanation: Running nvidia-smi inside the container verifies that the GPU is visible and accessible to the containerized application.
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