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NVIDIA AI Infrastructure Sample Questions:

1. An AI cluster needs to transmit data at 200Gbps over a distance of 2km using single-mode fiben Considering cost and performance, which transceiver type is the most appropriate?

A) 200GBASE-ER4
B) 200GBASE-DR4
C) 200GBASE-CR4
D) 200GBASE-SR4
E) 200GBASE-LR4

2. You're working with a large dataset of microscopy images stored as individual TIFF files. The images are accessed randomly during a training job. The current storage solution is a single HDD. You're tasked with improving data loading performance. Which of the following storage optimizations would provide the GREATEST performance improvement in this specific scenario?

A) Replacing the HDD with a RAID 5 array of HDDs.
B) Replacing the HDD with a single NVMe SS
C) Implementing data deduplication on the storage volume.
D) Migrating the data to a large, sequential HDD.
E) Compressing the TIFF files using a lossless compression algorithm.

3. You are deploying an NVIDIA GPU-accelerated application in a virtualized environment using vGPU. How does vGPU technology impact power and cooling considerations compared to a bare-metal deployment, and what specific monitoring metrics become crucial?

A) vGPU deployments typically require less power and cooling than bare-metal, as resources are shared. The host server's overall power consumption becomes the primary monitoring metric.
B) vGPU deployments have no significant impact on power and cooling requirements compared to bare-metal. Standard GPU temperature and power draw metrics are sufficient.
C) vGPU deployments require specialized cooling solutions that are not needed for bare metal setups.
D) vGPU deployments can lead to higher overall power consumption and concentrated heat generation on the host server due to resource consolidation. Monitoring metrics like GPU utilization per VM, vGPU frame rate, and host server thermal headroom become crucial.
E) vGPU deployments eliminate the need for GPU monitoring, as the virtualization layer handles all power and cooling management.

4. You are building a cloud-native application that uses both CPU and GPU resources. You want to optimize resource utilization and cost by scheduling CPU-intensive tasks on nodes without GPUs and GPU-intensive tasks on nodes with GPUs. How would you achieve this node selection and workload placement in Kubernetes?

A) Use node affinity rules to schedule CPU-intensive tasks on nodes without GPUs and GPU-intensive tasks on nodes with GPUs.
B) Use node affinity rules to schedule CPU-intensive tasks on nodes with GPUs and GPU-intensive tasks on nodes without GPUs.
C) Use taints and tolerations to dedicate nodes without GPUs to CPU-intensive tasks and nodes with GPUs to GPU-intensive tasks.
D) Use labels to identify the CPU and GPU-intensive nodes.
E) Use resource quotas to limit the CPU resources available on nodes with GPUs and the GPU resources available on nodes without GPUs.

5. An AI inference workload running on a server equipped with NVIDIA TensorRT is experiencing intermittent performance degradation. Profiling reveals potential issues with GPU utilization. Which command-line tool is best suited to provide real-time GPU performance metrics, including utilization, memory usage, and power consumption, during workload execution?

A) nvprof
B) gpustat
C) nvitop
D) tegrastats
E) nvidia-smi

Solutions: