An agent is tasked with solving a series of complex mathematical problems that require external tools to find information. It often struggles to keep track of intermediate steps and reasoning.
Which prompting technique would be MOST effective in improving the agent's clarity and reducing errors in its reasoning?

• A. Zero-shot CoT
• B. Symbolic Planning
• C. Multi-Plan Generation
• D. ReAct

You're deploying a healthcare-focused agentic AI system that helps doctors make treatment recommendations based on patient records. The agent's reasoning is not exposed to users, and its decisions sometimes differ from clinical guidelines.
What safety and compliance mechanisms should be in place? (Choose two.)

• A. Exempt the model from compliance if it improves outcomes
• B. Obfuscate decision logic to protect proprietary methods
• C. Require model explainability or traceability for all outputs
• D. Prioritize autonomous speed of decision over explainability
• E. Allow overrides by human doctors to maintain accountability

You're evaluating the performance of a tool-using agent (e.g., one that issues API calls or executes functions).
From the list below, what are two important features to evaluate? (Choose two.)

• A. Task completion rate
• B. Tool use rate
• C. Tokens per second
• D. Tool use accuracy

An AI engineer at an oil and gas company is designing a multi-agent AI system to support drilling operations.
Different agents are responsible for subsurface modeling, risk analysis, and resource allocation. These agents must share operational context, reason through interdependent planning steps, and justify their collaborative decisions using structured, transparent logic. The architecture must support memory persistence, sequential decision-making and chain-of-thought prompting across agents.
Which implementation best supports this design?

• A. Fine-tune separate NeMo models for each agent role using LoRA, with pre-scripted action flows deployed via TensorRT for latency reduction.
• B. Orchestrate NeMo agents via Triton, use vector memory for shared context, ReAct planning, and NeMo Guardrails for reasoning.
• C. Use stateless LLM endpoints behind an API gateway and pass shared prompts across agents to simulate context and reasoning.
• D. Use LangChain to coordinate third-party agent APIs and store shared information in external memory, with logic encoded in static prompt chains.

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