What is the NVIDIA NCA-AMAD certification?

NCA-AMAD (NVIDIA-Certified Associate: AI Models and Application Development) validates foundational skills for building, customizing, deploying and evaluating AI models and applications on NVIDIA's platform, including retrieval pipelines, inference optimization and agentic systems. NVIDIA designed it for entry-level AI developers.

How many questions does the NCA-AMAD exam have and how long is it?

NVIDIA lists 50 to 60 multiple-choice questions in 1 hour, taken online through Certiverse with pass/fail scoring. NVIDIA's exam details also list a hands-on lab. The passing score is not published.

How much does the NCA-AMAD exam cost?

The exam costs $125 USD per attempt. The certification is valid for two years, and you recertify by taking the exam again.

Which topics does NCA-AMAD cover?

Eight sections: Agentic AI (17%), Data Processing and Data Flywheel (17%), Inference Concepts (15%), Model Customization and Fine-Tuning Concepts (14%), Models and Model Types (11%), NVIDIA AI Enterprise and Blueprints Overview (10%), Sizing and Configuration (10%) and Physical AI (6%).

How hard is the NCA-AMAD exam?

It is an associate-level exam, but it is broad: agents, data pipelines, LLM serving, fine-tuning, model selection, NVIDIA's software stack, robotics simulation and deployment sizing all appear. NVIDIA's sample questions are short scenarios, so you need to pick the right technique for a situation as well as know the terms.

What do the Preporato practice exams for NCA-AMAD include?

Six timed practice exams of 60 questions each, 360 in total, with each exam weighted like NVIDIA's eight sections. Every option has an explanation, and every question links to the NVIDIA documentation behind it. Each exam also includes six Select TWO questions for extra depth. The first five questions of each exam are free to try.

How should I prepare for NCA-AMAD?

NVIDIA recommends its AI Model and Application Development Fundamentals course and lists suggested reading for each section on the exam page. Then take timed practice exams, review every explanation, and book the exam once you score 80% or more on several of them.

NVIDIA· Associate

NVIDIA-Certified Associate: AI Models and Application Development

NCA-AMAD

The NCA-AMAD exam tests the foundations of building AI applications on NVIDIA's platform: agents, data pipelines and flywheels, inference, model customization, model selection, NVIDIA AI Enterprise and Blueprints, physical AI, and deployment sizing. On preporato.com you prepare with timed practice exams and an explanation for every question.

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Practice exam #6 across all eight NCA-AMAD sections, including hierarchical agents and reflection, Triton and quantization choices, DoRA and LoRA, and Helm upgrades.

60 questions

60 minutes

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Why Get This Certification?

Skills You'll Validate

Designing agents with reasoning patterns, tools and MCP, memory, guardrails and human oversight
Building data pipelines and data flywheels: curation, PII handling, evaluation and production monitoring
Optimizing LLM inference with batching, KV and prefix caching, quantization, parallelism and disaggregated serving
Customizing models with prompting, RAG, LoRA and other PEFT methods, preference optimization and distillation
Selecting models by task, modality, size, license, context window and evaluation results
+3 more skills

Career Benefits

Target Roles

AI Application DeveloperMachine Learning EngineerSoftware Engineer moving into AIMLOps Engineer

Exam Topics & Domains

The certification exam evaluates your knowledge across 8 key competency areas:

Technologies & Tools Covered

NVIDIA NIMNVIDIA NeMo (Curator, Customizer, Evaluator, Guardrails, Retriever)NVIDIA NeMo Agent ToolkitNVIDIA NemoClawNVIDIA Nemotron modelsNVIDIA TensorRT-LLMNVIDIA Dynamo and Dynamo-TritonNVIDIA AI EnterpriseNVIDIA BlueprintsNVIDIA RAPIDSNVIDIA CosmosNVIDIA Isaac Sim, Isaac Lab and Isaac GR00TNVIDIA OmniverseNVIDIA Run:aiNVIDIA Mission ControlNVIDIA DCGM

What You'll Be Able To Do

After earning this certification, you'll be able to:

Choose between a single model call, a workflow and an agent, and design the agent's tools, memory and guardrails
Prepare data for training, retrieval and evaluation, and run a data flywheel on production feedback
Explain where inference time and cost go, and pick the optimization that fits a latency or throughput goal
Decide between prompting, RAG and fine-tuning, and pick a fine-tuning method for the data you have
Select a model by task, modality, size, license and measured results
Map business scenarios to NVIDIA AI Enterprise components, NIM microservices and Blueprints
Size GPUs and replicas for a workload, and monitor and operate it in production

Why This Certification Matters

Skills You'll Validate

Designing agents with reasoning patterns, tools and MCP, memory, guardrails and human oversight
Building data pipelines and data flywheels: curation, PII handling, evaluation and production monitoring
Optimizing LLM inference with batching, KV and prefix caching, quantization, parallelism and disaggregated serving
Customizing models with prompting, RAG, LoRA and other PEFT methods, preference optimization and distillation
Selecting models by task, modality, size, license, context window and evaluation results
Building on NVIDIA AI Enterprise, NIM microservices and NVIDIA Blueprints
+2 more skills

Career Benefits

Target Roles

AI Application DeveloperMachine Learning EngineerSoftware Engineer moving into AIMLOps Engineer+1

Industries Actively Hiring

Software and SaaSFinancial ServicesHealthcareManufacturing and RoboticsTelecommunicationsConsulting and System Integration

Frequently Asked Questions

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