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What benefits does a Kubernetes deployment offer over Slurm?

• A. Kubernetes provides autoscaling, auto-restarts, dynamic task scheduling, error isolation with containers, and integrated monitoring.
• B. Kubernetes is the best option for both training and inference, offering advantages for resource management and workload visibility over traditional HPC schedulers like Slurm.
• C. Kubernetes is more optimized for batch jobs to achieve high throughput, and also provides for monitoring and failover in large-scale workloads.

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?

• A. Deploy all agents on a single large GPU instance without containerization, scaling compute by upgrading to larger GPU instances when needed.
• B. Deploy agents directly on individual NVIDIA RTX workstations without containerization or orchestration, relying on load balancers with round-robin for traffic distribution.
• C. Deploy NVIDIA NIM microservices on Kubernetes with auto-scaling capabilities, utilizing NVIDIA NIM Operator for lifecycle management and horizontal pod autoscaling based on custom metrics.
• D. Deploy each agent on dedicated NVIDIA DGX systems with manual scaling based on previous days traffic predictions and static resource allocation for peak loads.

Which two coordination patterns are MOST effective for implementing a multi-agent system where agents have different specializations (Research Analyst, Content Writer, Quality Validator)?

• A. Peer-to-peer coordination with consensus mechanisms
• B. Random task distribution with load balancing
• C. Hierarchical coordination with crew-based task delegation
• D. Sequential pipeline coordination with crew-based structured handoffs

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?

• A. Adding more prompt examples to clarify privacy rules
• B. Enabling full session logging with audit trail metadata
• C. Increasing model temperature for diverse interpretations
• D. Masking all output with a profanity and PII detector

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