Question 1

An engineer has created a working AI agent solution providing helpful services to users. However, during live testing, the AI agent does not perform tasks consistently. Which two potential solutions might help with this issue? (Choose two.)

Options :

A : Remove schema validations and assertions on tool outputs to avoid inconsistency.

B : Increase randomness (e.g., temperature) and remove fixed seeds to avoid determinism.

C : Identify where dividing the tasks into subtasks and handling them by multiple agents can help.

D : Refine the prompt given to the AI Agent; be clear on objectives

Answer: C,D

Question 2

A health assistant agent has been running on production environment for several weeks. The compliance team wants to audit how personal health data has been processed. Which operational feature supports this requirement?

Options :

A : Adding more prompt examples to clarify privacy rules

B : Masking all output with a profanity and PII detector

C : Increasing model temperature for diverse interpretations

D : Enabling full session logging with audit trail metadata

Answer: D

Question 3

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.)

Options :

A : Maintain consistent resource allocation across all service hours, for a more precise view of baseline traffic impact on long-term infrastructure efficiency.

B : Scale agent infrastructure based on aggregate performance trends, using system-wide monitoring toolsto identify broader optimization patterns across resources.

C : Deploy agents with configurable scaling workflows, allowing analysis of resource adjustment strategies and their effects on service stability during variable demand periods.

D : Deploy distributed tracing with cost attribution per agent type, correlating resource consumption withbusiness value metrics to identify optimization opportunities in agent deployment strategies.

E : Implement comprehensive workload profiling using NVIDIA Nsight to analyze GPU utilization patterns, identify underutilized resources, and optimize batch sizing for dynamic scaling withKubernetes HPA.

Answer: D,E

Question 4

A financial services company is deploying a multi-agent customer service system consisting of three specialized agents: a reasoning LLM for complex queries, an embedding agent for document retrieval, and a re-ranking agent for result optimization. The system experiences significant traffic variations, with peak loads during business hours (10x normal traffic) and minimal usage overnight. The company needs a deployment solution that can handle these fluctuations cost-effectively while maintaining sub-second response times during peak periods. Which NVIDIA infrastructure approach would provide the MOST cost-effective and scalable deployment solution for this variable-load multi-agent system?

Options :

A : Deploy agents directly on individual NVIDIA RTX workstations without containerization ororchestration, relying on load balancers with round-robin for traffic distribution.

B : Deploy each agent on dedicated NVIDIA DGX systems with manual scaling based on previous daystraffic predictions and static resource allocation for peak loads.

C : Deploy NVIDIA NIM microservices on Kubernetes with auto-scaling capabilities, utilizing NVIDIANIM Operator for lifecycle management and horizontal pod autoscaling based on custom metrics.

D : Deploy all agents on a single large GPU instance without containerization, scaling compute byupgrading to larger GPU instances when needed.

Answer: C

Question 5

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?

Options :

A : Leverage NVIDIA NeMo Framework with modular memory management, integrating conversationalstate tracking, knowledge graphs, and vector store retrieval, while using LoRA-tuned models to adaptresponses overtime.

B : Leverage RAPIDS cuDF for memory tracking by streaming multi-turn conversation logs as GPUresident data frames, assuming transactional history can be recalled and reasoned over using dataframeoperations.

C : Rely exclusively on TensorRT to encode all prior knowledge into compiled model weights, allowinginference-only execution with no external memory dependencies across sessions.

D : Leverage NVIDIA Triton Inference Server with dynamic batching to cache session-level inputsbetween inference calls, and use an external Redis store for long-term memory.

Answer: A

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