Which of the following features of GPUs is most crucial for accelerating AI workloads, specifically in the context of deep learning?

• A.Large amount of onboard cache memory
• B.Ability to execute parallel operations across thousands of cores
• C.Lower power consumption compared to CPUs
• D.High clock speed

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Your AI infrastructure team is deploying a large NLP model on a Kubernetes cluster using NVIDIA GPUs.
The model inference requires low latency due to real-time user interaction. However, the team notices occasional latency spikes. What would be the most effective strategy to mitigate these latency spikes?

• A.Deploy the Model on Multi-Instance GPU (MIG) Architecture
• B.Use NVIDIA Triton Inference Server with Dynamic Batching
• C.Increase the Number of Replicas in the Kubernetes Cluster
• D.Reduce the Model Size by Quantization

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What is a common tool for container orchestration in AI clusters?

• A.Kubernetes
• B.MLOps
• C.Slurm
• D.Apptainer

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What is one of the primary benefits of using the NVIDIA GPU Operator in Kubernetes environments?

• A.It increases the processing power of CPUs within the Kubernetes cluster.
• B.It automatically updates the Kubernetes version across all nodes.
• C.It provides automatic scaling of NVIDIA GPU resources based on application demand.
• D.It simplifies the management and deployment of NVIDIA GPU software components.

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Which function is unique to workload managers in AI infrastructure (vs. general OS schedulers)?

• A.Enabling manual network configuration.
• B.Increasing user privileges automatically.
• C.Granular control over resource allocation.
• D.Selecting compute node vendors for procurement.

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