최신 NCP-AAI 무료덤프 - NVIDIA Agentic AI

A. Thought -- > Answer -- > Action -- > Observation

B. Thought -- > Action -- > Observation -- > Thought -- > Action -- > Observation -- > Answer

C. Observation -- > Thought -- > Action -- > Observation -- > Thought -- > Action -- > Answer

D. Action -- > Thought -- > Observation -- > Action -- > Thought -- > Observation -- > Answer

A. To establish rules and constraints based on the meaning of user queries and generated responses.

B. To automatically translate all LLM responses into multiple languages for improved user comprehension.

C. To eliminate all potential harmful entries from the vector database.

D. To filter out all queries containing specific keywords that have been flagged as problematic.

A. Test only final output accuracy as this will automatically include intermediate reasoning steps, decomposition quality, and prompt structure effectiveness for complex workflows.

B. Rely on generic prompt templates which are by default already optimized for general use, instead of tailoring them to financial terminology, calculation needs, or specialized multi-step analysis patterns.

C. Implement systematic prompt testing with chain-of-thought reasoning templates, step-by-step decomposition analysis, and success rate tracking across tasks of varying complexity.

D. Focus primarily on response speed optimization as a primary focus over reasoning quality, step completion accuracy, and prompt clarity for complex analytical requirements.

A. Automatically translating and integrating all retrieved chunks into a single language.

B. Blending information from multiple retrieved chunks into a single response generated by the LLM.

C. Creating a separate, dedicated database for storing all the retrieved chunks.

D. Minimizing the need for retrieval, allowing the LLM to generate responses directly from its internal knowledge.

A. Deploy agents directly on individual NVIDIA RTX workstations without containerization or orchestration, 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 days traffic predictions and static resource allocation for peak loads.

C. Deploy all agents on a single large GPU instance without containerization, scaling compute by upgrading to larger GPU instances when needed.

D. 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.

A. Adjust generation parameters to produce more predictable responses

B. Restrict available tools to reduce decision complexity

C. Cache recent API results to limit unnecessary repeated calls

D. Add retries with exponential backoff and set request timeouts

A. Obfuscate decision logic to protect proprietary methods

B. Allow overrides by human doctors to maintain accountability

C. Exempt the model from compliance if it improves outcomes

D. Prioritize autonomous speed of decision over explainability

E. Require model explainability or traceability for all outputs

A. Agents optimize for execution speed under fixed input-output mappings, while workflows prioritize goal alignment through adaptive reasoning and memory mechanisms.

B. Workflows emphasize parallelism and distributed coordination of processes, while agents emphasize serialization and isolated problem solving.

C. Workflows provide deterministic task sequencing with conditional branching, while agents adapt decisions dynamically based on goals, context, and environment feedback.

A. Relying solely on offline benchmarks without incorporating live user feedback during tuning

B. Implementing an evaluation framework that quantifies task efficiency and incorporates human-in-the- loop feedback

C. Tuning model parameters once before deployment to maximize initial accuracy

D. Comparing agents on isolated tasks without standardized benchmarking pipelines

A. Implementing a single, centralized database for all data, updated with a synchronous polling mechanism for the LLM to retrieve the latest information.

B. Use a loosely coupled, event-driven micro-service architecture where separate services handle data indexing, retrieval, and LLM prompting.

C. Employing a consolidated architecture with a large service handling all data retrieval and LLM interaction. This ensures consistent performance and simplifies debugging.

D. Using a synchronous, block-level approach, where the LLM continuously monitors the database for updates and retrieves the entire dataset with each prompt.