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NCA-AMAD: NVIDIA AI Model & App Dev Exam Prep
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NCA-AMAD: NVIDIA AI Model & App Dev Exam Prep
Exam-focused preparation covering agentic AI, RAG, fine-tuning, inference, and the NVIDIA AI stack
Created byAseem Mankotia
Last updated 10/2026
English
What you'll learn
- Design and evaluate agentic AI workflows using tool use, planning loops, and multi-agent orchestration
- Build RAG pipelines and apply the data flywheel concept for continuous model improvement
- Compare inference optimization techniques including batching, quantization, and KV caching
- Select the right customization technique (prompting, RAG, PEFT/LoRA, fine-tuning) for a use case
- Map NVIDIA AI Enterprise, NIM, NeMo, and Blueprints onto an AI application stack
Explore related topics
Course content
11 sections • 10 lectures • 2h 48m total length
Requirements
- Basic Python and core AI/ML vocabulary (models, training, inference). No formal prerequisites - NCA-AMAD has an exam-only path.
Description
This course contains the use of artificial intelligence.
NCA-AMAD is NVIDIA's newest associate credential, built for AI developers who need to prove they understand how modern AI models are customized, served, and consumed as real applications on the NVIDIA stack. Instructor Aseem Mankotia walks through all eight official exam domains in their published weight order, starting with the two heaviest topics, Agentic AI and Data Processing and the Data Flywheel, which together make up over a third of the exam, before moving through Inference Concepts, Model Customization and Fine-Tuning, Models and Model Types, NVIDIA AI Enterprise and Blueprints, Sizing and Configuration, and Physical AI.
Each chapter maps directly to an exam objective and pairs concept explanations with concrete, no-cloud-required hands-on exercises: sketching an agentic workflow with tool use and planning loops, designing a RAG ingestion pipeline and data flywheel, choosing between prompting, RAG, LoRA/PEFT, and full fine-tuning for a given budget, sizing GPU memory for training versus inference, and mapping NeMo, NIM microservices, Triton, and NVIDIA Blueprints onto a sample application. Scenario-based practice questions throughout mirror the style and difficulty of the real exam, with explanations for every option so you understand not just the correct answer but why the distractors are wrong.
The course closes with a full timed practice exam simulation across all eight domains and a 60-minute, 50-60-question time-management strategy for the Certiverse-delivered test. It is a strong on-ramp into NVIDIA's broader AI-developer track, and learners frequently pair it with the NVIDIA associate courses (NCA-AIIO, NCA-GENL, NCA-GENM, NCA-ADS) and the NCP professional track as their next step.
AI content disclosure: This course was produced with the assistance of artificial intelligence tools. Lecture narration is AI-voice generated, and lecture scripts, slides, and practice questions were drafted with AI assistance, then reviewed and curated by the instructor for technical accuracy and alignment with the official NCA-AMAD exam guide.
Who this course is for:
- Entry-level AI developers, software engineers, and technical practitioners preparing for the NCA-AMAD exam or wanting to understand how modern AI models are customized, served, and consumed as applications on the NVIDIA stack.