Free Practice Questions for NVIDIA-Certified Professional: Agentic AI Certification

    🔄 Last checked for updates April 27th, 2026

    Study with 340 exam-style practice questions designed to help you prepare for the NVIDIA-Certified Professional: Agentic AI.

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    Exam Details

    Key information about NVIDIA-Certified Professional: Agentic AI

    Official study guide

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    Question formats CertSafari offers
    • Multiple choice
    prerequisites:

    2-3 years in AI and machine learning roles; experience with production-level agentic AI projects (e.g., chatbots, workflow automation)

    target audience:

    Intermediate practitioner adept at designing, evaluating, and deploying autonomous AI systems, including constructing resilient, secure, and trustworthy agentic solutions such as customer/employee assistants, automated meeting companions, and productivity tools.

    Exam Topics & Skills Assessed

    Skills measured (from the official study guide)

    Domain 1: Agent Architecture and Design

    Subdomain 1.1: Design user interfaces for intuitive human-agent interaction.

    Design user interfaces for intuitive human-agent interaction.

    Subdomain 1.2: Implement reasoning and action frameworks (e.g., ReAct).

    Implement reasoning and action frameworks (e.g., ReAct).

    Subdomain 1.3: Configure agent-to-agent communication protocols for collaboration.

    Configure agent-to-agent communication protocols for collaboration.

    Subdomain 1.4: Manage short-term and long-term memory for context retention.

    Manage short-term and long-term memory for context retention.

    Subdomain 1.5: Orchestrate multi-agent workflows and coordination.

    Orchestrate multi-agent workflows and coordination.

    Subdomain 1.6: Apply logic trees, prompt chains, and stateful orchestration for multi-step reasoning.

    Apply logic trees, prompt chains, and stateful orchestration for multi-step reasoning.

    Subdomain 1.7: Integrate knowledge graphs to enable relational reasoning.

    Integrate knowledge graphs to enable relational reasoning.

    Subdomain 1.8: Ensure adaptability and scalability of the agent’s architecture.

    Ensure adaptability and scalability of the agent’s architecture.

    Domain 2: Agent Development

    Subdomain 2.1: Engineer prompts and dynamic prompt chains for reliable performance.

    Engineer prompts and dynamic prompt chains for reliable performance.

    Subdomain 2.2: Integrate generative and multimodal models (text, vision, audio).

    Integrate generative and multimodal models (text, vision, audio).

    Subdomain 2.3: Build and connect custom tools, APIs, and functions for external system interaction.

    Build and connect custom tools, APIs, and functions for external system interaction.

    Subdomain 2.4: Implement error handling (retry logic, graceful failure recovery).

    Implement error handling (retry logic, graceful failure recovery).

    Subdomain 2.5: Develop dynamic conversation flows with real-time streaming and feedback mechanisms.

    Develop dynamic conversation flows with real-time streaming and feedback mechanisms.

    Subdomain 2.6: Evaluate and refine agent decision-making strategies.

    Evaluate and refine agent decision-making strategies.

    Domain 3: Evaluation and Tuning

    Subdomain 3.1: Implement evaluation pipelines and task benchmarks to measure performance.

    Implement evaluation pipelines and task benchmarks to measure performance.

    Subdomain 3.2: Compare agent performance across tasks and datasets.

    Compare agent performance across tasks and datasets.

    Subdomain 3.3: Collect and integrate structured user feedback for iterative improvements.

    Collect and integrate structured user feedback for iterative improvements.

    Subdomain 3.4: Tune model parameters (e.g., accuracy, latency-efficiency trade-offs).

    Tune model parameters (e.g., accuracy, latency-efficiency trade-offs).

    Subdomain 3.5: Analyze evaluation results to guide targeted optimization.

    Analyze evaluation results to guide targeted optimization.

    Domain 4: Deployment and Scaling

    Subdomain 4.1: Deploy and orchestrate multi-agent systems at production scale.

    Deploy and orchestrate multi-agent systems at production scale.

    Subdomain 4.2: Apply MLOps practices for continuous integration and continuous delivery (CI/CD) workflows, monitoring, and governance.

    Apply MLOps practices for continuous integration and continuous delivery (CI/CD) workflows, monitoring, and governance.

    Subdomain 4.3: Profile performance and reliability under distributed system loads.

    Profile performance and reliability under distributed system loads.

    Subdomain 4.4: Scale deployments using containerization (Docker, Kubernetes) with load balancing.

    Scale deployments using containerization (Docker, Kubernetes) with load balancing.

    Subdomain 4.5: Optimize deployment costs while ensuring high availability.

    Optimize deployment costs while ensuring high availability.

    Domain 5: Cognition, Planning, and Memory

    Subdomain 5.1: Implement memory mechanisms for short- and long-term context retention.

    Implement memory mechanisms for short- and long-term context retention.

    Subdomain 5.2: Apply reasoning frameworks (chain-of-thought, task decomposition).

    Apply reasoning frameworks (chain-of-thought, task decomposition).

    Subdomain 5.3: Engineer planning strategies for sequential and multi-step decision-making.

    Engineer planning strategies for sequential and multi-step decision-making.

    Subdomain 5.4: Manage stateful orchestration to coordinate complex tasks and knowledge retention.

    Manage stateful orchestration to coordinate complex tasks and knowledge retention.

    Subdomain 5.5: Adapt reasoning strategies based on prior experiences and feedback.

    Adapt reasoning strategies based on prior experiences and feedback.

    Domain 6: Knowledge Integration, and Data Handling

    Subdomain 6.1: Implement retrieval pipelines (RAG, embedded search, hybrid approaches).

    Implement retrieval pipelines (RAG, embedded search, hybrid approaches).

    Subdomain 6.2: Configure and optimize vector databases for fast retrieval.

    Configure and optimize vector databases for fast retrieval.

    Subdomain 6.3: Build extract, transform, and load (ETL) pipelines to integrate enterprise or client data sources.

    Build extract, transform, and load (ETL) pipelines to integrate enterprise or client data sources.

    Subdomain 6.4: Conduct data quality checks, augmentation, and preprocessing.

    Conduct data quality checks, augmentation, and preprocessing.

    Subdomain 6.5: Enable real-time access and reasoning over structured and unstructured knowledge.

    Enable real-time access and reasoning over structured and unstructured knowledge.

    Domain 7: NVIDIA Platform Implementation

    Subdomain 7.1: Integrate NVIDIA NeMo Guardrails for compliance and safety enforcement.

    Integrate NVIDIA NeMo Guardrails for compliance and safety enforcement.

    Subdomain 7.2: Deploy NVIDIA NIM microservices for high-performance inference.

    Deploy NVIDIA NIM microservices for high-performance inference.

    Subdomain 7.3: Optimize workflows with the NVIDIA NeMo Agent Toolkit.

    Optimize workflows with the NVIDIA NeMo Agent Toolkit.

    Subdomain 7.4: Leverage NVIDIA TensorRT-LLM and Triton Inference Server for latency reduction.

    Leverage NVIDIA TensorRT-LLM and Triton Inference Server for latency reduction.

    Subdomain 7.5: Manage and optimize multimodal input pipelines on NVIDIA hardware.

    Manage and optimize multimodal input pipelines on NVIDIA hardware.

    Domain 8: Run, Monitor, and Maintain

    Subdomain 8.1: Define monitoring dashboards and reliability metrics.

    Define monitoring dashboards and reliability metrics.

    Subdomain 8.2: Track logs, errors, and anomalies for root cause diagnosis.

    Track logs, errors, and anomalies for root cause diagnosis.

    Subdomain 8.3: Continuously benchmark deployed agents against prior versions.

    Continuously benchmark deployed agents against prior versions.

    Subdomain 8.4: Implement automated tuning, retraining, and versioning in production.

    Implement automated tuning, retraining, and versioning in production.

    Subdomain 8.5: Ensure continuous uptime, transparency, and trust in live deployments.

    Ensure continuous uptime, transparency, and trust in live deployments.

    Domain 9: Safety, Ethics, and Compliance

    Subdomain 9.1: Design and enforce system security and audit trails.

    Design and enforce system security and audit trails.

    Subdomain 9.2: Integrate compliance guardrails (privacy, enterprise policy).

    Integrate compliance guardrails (privacy, enterprise policy).

    Subdomain 9.3: Mitigate bias and toxicity in outputs.

    Mitigate bias and toxicity in outputs.

    Subdomain 9.4: Deploy layered safety frameworks (filters, escalation protocols).

    Deploy layered safety frameworks (filters, escalation protocols).

    Subdomain 9.5: Ensure compliance with licensing and regulatory standards.

    Ensure compliance with licensing and regulatory standards.

    Domain 10: Human-AI Interaction and Oversight

    Subdomain 10.1: Build intuitive UIs with user-in-the-loop interaction.

    Build intuitive UIs with user-in-the-loop interaction.

    Subdomain 10.2: Design structured feedback loops that guide iterative agent improvements.

    Design structured feedback loops that guide iterative agent improvements.

    Subdomain 10.3: Implement transparency mechanisms (explainable reasoning, decision traceability).

    Implement transparency mechanisms (explainable reasoning, decision traceability).

    Subdomain 10.4: Enable human oversight and intervention for accountability and trust.

    Enable human oversight and intervention for accountability and trust.

    Techniques & products

    ReAct
    NVIDIA Agentic NeMo
    AI Virtual Assistants
    NVIDIA NIM Agent Blueprint
    Multi-Agent Frameworks
    Prompt Engineering
    Generative Models
    Multimodal Models
    APIs
    Error Handling
    Retry Logic
    Graceful Failure Recovery
    NVIDIA Triton Inference Server
    NVIDIA Agent Intelligence Toolkit
    LlamaIndex
    Milvus
    Circuit Breaker Pattern
    Transient Fault Handling
    Evaluation Pipelines
    Task Benchmarks
    Model Parameter Tuning
    MLOps Practices
    CI/CD Workflows
    Monitoring
    Governance
    Containerization
    Docker
    Kubernetes
    Load Balancing
    NVIDIA TensorRT-LLM
    NVIDIA DGX Cloud Benchmarking
    NVIDIA Nsight Systems
    Kube Prometheus for GPU Telemetry
    NVIDIA NeMo
    Large Language Models (LLMs)
    NeMo RL
    Jamba 1.5 LLMs
    AI Agent Memory
    Retrieval Augmented Generation (RAG)
    Embedded Search
    Vector Databases
    ETL Pipelines
    Data Quality Checks
    Data Augmentation
    Data Preprocessing
    Fine-Tuning
    NVIDIA NeMo Guardrails
    NVIDIA NIM Microservices
    NVIDIA NeMo Agent Toolkit
    NVIDIA AIQ Toolkit
    NVIDIA Llama Nemotron API
    Llama-3.1-Nemotron-70B-Instruct
    LangChain Tracing Concepts
    LangChain Structured Outputs Concepts
    Smith LangChain Model Evaluation
    System Security
    Audit Trails
    Compliance Guardrails
    Bias Mitigation
    Toxicity Mitigation
    Layered Safety Frameworks
    Ethically Aligned Design
    Responsible AI
    Human-in-the-Loop AI
    Aporia: AI Guardrails
    Chain-of-Thought (CoT) Prompting

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