A technology startup is preparing to launch an AI agent platform to serve clients with unpredictable usage patterns. They face periods of high user activity and low demand, so their deployment approach must minimize wasted resources during slow times and automatically allocate more resources during busy periods
- all while keeping operational costs reasonable.
Given these requirements, which deployment strategy most effectively ensures both cost-effectiveness and adaptability for scaling agentic AI systems?

• A. Monitoring system logs for usage patterns and making infrastructure changes after monthly analysis
• B. Using fixed-size virtual machine clusters to guarantee consistent resource allocation at all times
• C. Scheduling periodic manual reviews to increase or decrease infrastructure based on predicted user numbers
• D. Implementing autoscaling policies in a container orchestration environment to automatically adjust resources according to workload changes

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You are tasked with deploying a multi-modal agentic system that must respond to user queries with minimal latency while maintaining guardrails for safe and context-aware interactions.
Which of the following configurations best leverages NVIDIA's AI stack to meet these requirements?

• A. Use NIM microservices for deployment, optionally use NeMo Guardrails unless one wants to minimize the inference overhead.
• B. Use NeMo Guardrails for safety, deploy the model with Triton Inference Server using default settings, and rely on hardware accelerators like GPU/TPU inference for cost efficiency.
• C. Integrate NeMo Guardrails, use Omniverse to generate synthetic data, configure NIM microservices for optimized inference, use TensorRT-LLM for deployment, and profile the system using NeMo Agent Toolkit for multi-modal support.
• D. Integrate NeMo Guardrails, configure NIM microservices for optimized inference, use TensorRT-LLM for deployment, and profile the system using Triton Inference Server with multi-modal support.

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When designing complex agentic workflows that include both sequential and parallel task execution, which orchestration pattern offers the greatest flexibility?

• A. Linear pipeline orchestration with a fixed task sequence
• B. Graph-based workflow orchestration incorporating conditional branches
• C. Event-driven orchestration that triggers tasks reactively, in series or in parallel

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When evaluating GPU utilization inefficiencies in deploying Llama Nemotron models across A100 and H100 clusters, which approaches help identify optimal resource allocation strategies? (Choose two.)

• A. Allocate all agents to Hl00 GPUs, allowing resource profiles to automatically adjust for model size and computational requirements.
• B. Profile resource utilization for each Nemotron variant and match models to appropriate GPU tiers.
• C. Allow Nemotron variants to profile actual workload characteristics and allocate resources based on observed demands.
• D. Assess concurrent execution capabilities by employing multi-instance GPU partitioning for varying workload types.

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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?

• A. Use a static assignment of requests across agents to maintain consistent agent operation and simplify coordination while scaling infrastructure resources as needed.
• B. Optimize GPU utilization frameworks with workload optimization separate from cost analysis, prioritizing resource performance for peak load scenarios in deployment.
• C. Deploy agents on a single machine to obtain a dimensioning baseline and thereby reduce setup complexity before expanding system scope.
• D. Implementing automated workload management and resource scheduling frameworks to optimize GPU utilization and maintain service availability.

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