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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Workload Management | 20% | - Framework and runtime configuration - Data pipeline and storage integration - Job scheduling and queue management - AI workload deployment and scaling |
| Topic 2: Installation and Deployment | 32% | - Container orchestration and resource management - AI cluster setup and configuration - Driver and firmware installation - NVIDIA software stack deployment |
| Topic 3: Troubleshooting and Optimization | 20% | - Performance monitoring and analysis - System health and reliability maintenance - Throughput and latency optimization - Fault diagnosis and resolution |
| Topic 4: Management | 28% | - Access control and security policies - GPU cluster and compute node management - Software and container lifecycle management - Resource allocation and scheduling |
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NEW QUESTION # 86
You're optimizing a BCM pipeline that processes images. You notice that the CPU is consistently at 100% utilization, while the GPU is underutilized. Which optimization strategy is MOST likely to improve performance?
Answer: D
Explanation:
Offloading CPU-intensive tasks to the underutilized GPU and implementing asynchronous data transfer are both effective strategies to reduce CPU bottleneck and utilize GPU resources more efficiently.
NEW QUESTION # 87
A Slurm user needs to display real-time information about the running processes and resource usage of a Slurm job.
Which command should be used?
Answer: B
Explanation:
The Slurm command sstat is designed to provide real-time statistics about running jobs, including process-level details and resource usage such as CPU, memory, and GPU utilization. Using sstat
-j <jobid> or sstat -j <jobid.step> allows monitoring of active job resource consumption.
NEW QUESTION # 88
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: A
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 # 89
You are tasked with configuring MIG in a Kubernetes cluster to support multiple AI workloads with varying GPU resource demands. You want to define a Kubernetes resource quota that limits the total amount of GPU memory available to a specific namespace. How can you achieve this using NVIDIA's Kubernetes integration?
Answer: E
Explanation:
With the NVIDIA GPU Operator, Kubernetes exposes MIG resources as custom resources, including 'nvidia.com/gpu.memory'. You can define resource quotas that limit the total amount of GPU memory requested by pods in a namespace using this resource type. Other options are inaccurate or do not directly address the requirement.
NEW QUESTION # 90
You are deploying a PyTorch container from NGC that utilizes Tensor Cores. How can you verify that Tensor Cores are being effectively used during inference?
Answer: D,E
Explanation:
B and E are correct. 'nvidia-smi' shows GPU utilization, including Tensor Core activity. Nsight Systems provides detailed profiling information, allowing you to identify specific Tensor Core operations. A is unreliable as log messages may not always be present. C refers to training, not inference. D is impractical without access to the container's source code.
NEW QUESTION # 91
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