NVIDIA NCP-AAI Actual Tests : Agentic AI

Walking into the NCP-AAI exam cold is a risk you do not need to take. The Actual4test test engine simulates the real exam environment, letting you rehearse the NVIDIA Agentic AI experience with 123 timed practice questions.

NVIDIA NCP-AAI Exam Overview:

NVIDIA NCP-AAI Exam Syllabus Topics:

Everything You Want to Know About the NVIDIA NCP-AAI Exam

NVIDIA Agentic AI Sample Questions:

Question 1

An AI architect at a national healthcare provider is maintaining an agentic AI system. The system must monitor model and system performance in real time, raise alerts on failures or anomalies, manage version control and rollback of diagnostic models, and provide transparent insight into agent behavior during patient care workflows.
Which operational approach best supports these requirements using the NVIDIA AI stack?

A. Expose agents as stateless NVIDIA API endpoints and monitor activity through application logs, with model versions tracked in a Git-based script repository.
B. Containerize each agent in NIM with basic health checks running on cron jobs, and manage version rollback by swapping prebuilt container images.
C. Optimize all models with TensorRT and use periodic manual log reviews and NVIDIA shell scripts for detecting service anomalies and managing rollback.
D. Deploy agent models on NVIDIA Triton Inference Server with Prometheus and Grafana for performance alerting, and manage model lifecycle via NGC and the Triton model repository.

Question 2

Which two optimization strategies are MOST effective for improving agent performance on NVIDIA GPU infrastructure? (Choose two.)

A. Applying TensorRT-LLM optimizations to reduce inference latency by improving kernel efficiency and memory usage.
B. Manually tuning kernel launch parameters to optimize individual operations while overlooking overall pipeline performance dynamics.
C. Expanding GPU memory capacity to support larger models, assuming this alone guarantees meaningful performance improvements.
D. Using multi-GPU coordination to distribute workloads, enabling higher throughput and efficiency for scaling agent tasks.

Question 3

An enterprise wants their AI agent to support complex project management tasks. The agent should remember ongoing project details, adjust its plans based on new information, and break down large goals into actionable steps.
Which strategy best enables the AI agent to autonomously decompose tasks and adapt to new Information over time?

A. Predefining static workflows for each project type to guarantee consistent execution
B. Developing long-term knowledge retention strategies and dynamic state management for adaptive planning
C. Storing recent user interactions in a temporary cache for immediate retrieval
D. Applying rule-based logic to each new request isolated from previous project data

Question 4

In designing an AI workflow which of the following best describes a comprehensive approach to improving the performance of AI agents?

A. Implementing benchmarking pipelines, collecting user feedback, and tuning model parameters iteratively
B. Monitoring agents' throughput and time-to-first-token from the scoring engine
C. Implementing benchmarking pipelines, deploying physical agents and monitoring user engagement metrics
D. Implementing benchmarking pipelines and incorporating a dynamic dataset for a real-time fall-back

Question 5

You are tasked with comparing two agentic AI systems - System A and System B - both designed to generate marketing copy.
You've run identical prompts and have recorded the generated outputs.
To objectively assess which system is performing better, what is the most appropriate approach?

A. Gather ratings from a panel of users, with each rating marketing copy on a 1 to 5 scale for overall impression of relevance, creativity, and grammatical correctness.
B. Implement a benchmark pipeline that automatically compares the generated outputs using metrics like relevance, creativity, and grammatical correctness.
C. Measure the click-through rate for each system's marketing copy as the primary indicator of performance.
D. Implement a human-in-the-loop to subjectively rate each output on a scale of 1 to 5 based on the user's personal preference.

Solutions: