When evaluating a multi-agent customer service system experiencing unpredictable scaling costs and performance bottlenecks during peak hours, which analysis approaches effectively identify optimization opportunities for both infrastructure efficiency and service reliability? (Choose two.)
Answer(s): D,E
Distributed tracing with cost attribution reveals how each agent type consumes resources and contributes to cost, enabling precise identification of inefficiencies. GPU workload profiling with Nsight exposes utilization gaps and batching opportunities, allowing tuning of model execution and scaling behavior to eliminate performance bottlenecks and reduce unnecessary infrastructure usage.
When analyzing throughput bottlenecks in a multi-modal agent processing text, images, and audio, which Triton configuration evaluations identify optimization opportunities? (Choose two.)
Answer(s): A,B
Examining ensemble pipelines reveals where sequential processing unnecessarily limits throughput and where parallelization or optimized data transfer can increase efficiency. Profiling GPU memory patterns and tuning batching and concurrency settings allows Triton to better utilize hardware across multimodal workloads, uncovering concrete opportunities to reduce bottlenecks and improve throughput.
When analyzing performance bottlenecks in a multi-modal agent processing customer support tickets with text, images, and voice inputs, which evaluation approach most effectively identifies optimization opportunities?
Answer(s): B
Profiling end-to-end latency across modalities, evaluating model-switching overhead, and identifying batching opportunities directly exposes where the multi-modal pipeline is losing efficiency. Triton's dynamic batching analysis further reveals how throughput and latency can be optimized under real workloads, making this the most effective method for uncovering performance bottlenecks.
What benefits does a Kubernetes deployment offer over Slurm?
Answer(s): A
Kubernetes offers autoscaling, automatic restarts, flexible scheduling, container-level isolation, and built-in observability tools, making it better suited than Slurm for dynamic, service-oriented agentic AI workloads.
A company plans to launch a multi-agent system that must serve thousands of users simultaneously. The team needs to ensure the system remains reliable, scales efficiently as demand increases, and operates in a cost- effective manner.Which approach is most effective for achieving robust and scalable deployment of an agentic AI system in production?
Answer(s): D
Container orchestration with load balancing enables horizontal scaling, fault isolation, and efficient resource utilization, while continuous monitoring ensures the system adapts to changing demand. This combination provides the robustness and scalability required for large multi-agent production deployments.
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