A social media company wants to expand its agentic system to support global users, minimize downtime, and ensure smooth operation during usage spikes. The team is considering various deployment and scaling strategies to achieve these goals.Which solution most effectively supports reliable and scalable deployment for an agentic AI system serving a global user base?
Answer(s): B
A distributed, multi-region architecture with automated failover and dynamic resource allocation ensures high availability, low latency for global users, and stable scaling during usage spikes, making it the most effective strategy for reliable large-scale agentic system deployment.
A company is deploying a multi-agent AI system to handle large-scale customer interactions. They want to ensure the system is highly available, cost-effective, and scalable across multiple NVIDIA GPUs using container orchestration tools.Which practice is most crucial for successfully deploying and scaling an agentic AI system in production?
Answer(s): D
Automated workload management and resource scheduling ensure efficient GPU utilization, maintain high availability, and support cost-effective scaling across distributed agents. This is essential for production-grade multi-agent deployments running on container orchestration platforms.
You are deploying a multi-agent customer-support system on Kubernetes using NVIDIA GPU nodes and Triton Inference Server. Traffic spikes during product launches. You need <100ms response times, zero downtime, automatic GPU scaling, and full monitoring.Which deployment setup best achieves cost-effective, reliable, low-latency scaling?
Answer(s): C
A multi-zone GPU node pool with mixed GPU types ensures high availability, while combining Cluster Autoscaler and Horizontal Pod Autoscaler using GPU and latency metrics enables precise, low-latency autoscaling. DCGM with Grafana provides full GPU-level observability, supporting reliable, cost-effective, sub- 100ms performance during traffic spikes.
Which two deployment patterns are MOST suitable for scaling agentic workloads on NVIDIA Infrastructure? (Choose two.)
Answer(s): D,E
NIM provides containerized, GPU-optimized inference services ideal for scalable agent workloads, while Kubernetes with HPA enables automated, demand-driven scaling across GPU nodes. Together, they deliver flexible, efficient, production-grade scaling for agentic systems.
When evaluating an agent's degrading response times under increasing load, which analysis approach most effectively identifies scalability bottlenecks and optimization opportunities?
Distributed tracing combined with GPU utilization analysis and queuing-delay mapping reveals where bottlenecks emerge as load increases. This provides a complete view of scalability constraints across the end- to-end pipeline, enabling targeted optimization of the most impactful components.
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