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| Section | Objectives |
|---|---|
| Topic 1: NVIDIA AI Operations Tools | - Container and orchestration tools
|
| Topic 2: AI Infrastructure Troubleshooting | - Performance optimization
|
| Topic 3: Infrastructure Operations | - Security and access management
|
| Topic 4: Cluster and Workload Management | - Slurm administration
|
| Topic 5: AI Infrastructure Monitoring | - Monitor GPU resources
|
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NEW QUESTION # 78
You are tasked with configuring MIG on an NVIDIAA100 GPU for a mixed AI/HPC workload. You need to create two instances: one for a deep learning training job (requiring high memory bandwidth) and another for a molecular dynamics simulation (requiring high compute throughput). Which is the MOST optimal MIG configuration to create based on these workload requirements?
Answer: A
Explanation:
Deep learning training typically benefits from larger memory capacities and bandwidth. While molecular dynamics often leverages compute throughput. Therefore, allocating 3g.20gb for deep learning, with focus on memory, and 4g.20gb for molecular dynamics will better utilize computational resources based on the workload characteristics. The lg,2g options are too small, and 7g option might overcommit resources that other processes or users could need on the same node.
NEW QUESTION # 79
What must be done before installing new versions of DOCA drivers on a BlueField DPU?
Answer: A
Explanation:
Before installing new versions of DOCA drivers on NVIDIA BlueField DPUs, it is required to uninstall any previous versions of DOCA drivers to prevent conflicts and ensure a clean upgrade.
This ensures that the new installation is not affected by leftover files or configurations from earlier versions. Re-flashing firmware or disabling network interfaces is not always required before every drivern installation. Rebooting the host system might be recommended after installation but is not a prerequisite before installing drivers.
NEW QUESTION # 80
A system administrator needs to scale a Kubernetes Job to 4 replicas.
What command should be used?
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The correct command to scale a Kubernetes Job to a specific number of replicas iskubectl scale job -- replicas=4. This explicitly sets the number of desired pod instances for the Job resource. The other commands are either invalid (stretch), apply to Deployments rather than Jobs (autoscale deployment), or use incorrect syntax (-r).
NEW QUESTION # 81
A system administrator is troubleshooting a Docker container that is repeatedly failing to start.
They want to gather more detailed information about the issue by generating debugging logs.
Why would generating debugging logs be an important step in resolving this issue?
Answer: C
Explanation:
Generating debugging logs enables detailed visibility into the internal operations of the Docker daemon. These logs expose low-level errors, misconfigurations, and runtime issues that standard logs might not capture, making them essential for diagnosing why a container repeatedly fails to start.
NEW QUESTION # 82
You have successfully pulled a TensorFlow container from NGC and now need to run it on your stand- alone GPU-enabled server.
Which command should you use to ensure that the container has access to all available GPUs?
Answer: B
Explanation:
When running a GPU-enabled container directly on a server with Docker, the flag --gpus all is required to allow the container access to all GPUs on the host system. This ensures that the TensorFlow container can utilize GPU resources fully. The other options either do not specify GPU access correctly or are Kubernetes-specific commands.
NEW QUESTION # 83
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