Your agent is generating inconsistent and contradictory statements.
Which approach would be most suitable to improve the agent's output?

• A. Increasing the number of generated plans
• B. Employing Reflexion
• C. Using Decomposition-First Planning
• D. Decreasing the length of prompts

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. Scheduling periodic manual reviews to increase or decrease infrastructure based on predicted user numbers
• C. Using fixed-size virtual machine clusters to guarantee consistent resource allocation at all times
• D. Implementing autoscaling policies in a container orchestration environment to automatically adjust resources according to workload changes

In a global financial firm, an AI Architect is building a multi-agent compliance assistant using an agentic AI framework. The system must manage short-term memory for multi-turn interactions and long-term memory for persistent user and policy context. It should enable contextual recall and adaptation across sessions using NVIDIA's tool stack.
Which architectural approach best supports these requirements?

• A. Rely exclusively on TensorRT to encode all prior knowledge into compiled model weights, allowing inference-only execution with no external memory dependencies across sessions.
• B. Leverage NVIDIA Triton Inference Server with dynamic batching to cache session-level inputs between inference calls, and use an external Redis store for long-term memory.
• C. Leverage NVIDIA NeMo Framework with modular memory management, integrating conversational state tracking, knowledge graphs, and vector store retrieval, while using LoRA-tuned models to adapt responses overtime.
• D. Leverage RAPIDS cuDF for memory tracking by streaming multi-turn conversation logs as GPU- resident data frames, assuming transactional history can be recalled and reasoned over using dataframe operations.

An AI Engineer is analyzing a production agentic AI system's compliance with responsible AI standards.
Which evaluation approaches effectively identify potential safety vulnerabilities and ethical risks in multi- agent workflows? (Choose two.)

• A. Deploy multi-layered evaluation combining bias detection metrics (demographic parity, equalized odds) with adversarial testing to probe agent responses for harmful outputs across diverse user populations
• B. Implement comprehensive audit trails using NVIDIA NeMo Guardrails with semantic similarity checks, tracking agent decisions across conversation flows and evaluating policy violations through automated compliance scoring.
• C. Use user feedback as a primary signal for risk identification, emphasizing post-deployment observations and qualitative experience reports alongside operational monitoring.
• D. Emphasize latency metrics and throughput performance as key evaluation factors for safety vulnerabilities, providing a baseline for operational measures and resource allocation.