The selected option specifically A states “Design a modular multi-agent system where individual agents collaborate asynchronously using shared memory and structured messaging.”, which matches the operational requirement rather than a superficial wording match. Fraud monitoring needs specialization: transaction monitors, pattern analysts, memory stores, and escalation agents. Asynchronous collaboration prevents one slow analytical path from blocking the entire detection fabric. Option A fits the operating model because the problem describes an agent that must remain adaptive under changing inputs and infrastructure conditions. This lines up with NVIDIA guidance because NeMo Agent Toolkit is framework-agnostic and can orchestrate LangChain, CrewAI, LlamaIndex, Semantic Kernel, and custom Python agents behind a common workflow layer. The durable control mechanism is workflow graphs where agent responsibilities, inputs, and completion criteria are visible to both orchestration and evaluation layers. That is why the other options are traps: random routing or unstructured collaboration wastes specialization and makes coordination failures look like model hallucinations. For certification purposes, read the question as asking for controlled autonomy, not raw LLM creativity.