NCA-AMAD questions are short situations from the life of an AI application: an agent stalls in a tool loop, a summarization service spends its GPU time on long inputs, a robotics team needs a pretrained model for a humanoid. Each asks which technique, setting or NVIDIA product fits, and the wrong options are real techniques and real NVIDIA products in the wrong place. That is the pattern of the sample questions NVIDIA publishes for the exam.
A useful practice test trains that recognition across all eight sections, at the pace of the real exam (up to 60 questions in one hour, as of October 2026), and explains why each wrong option is wrong.
Two questions in the style of the exam
Both cover topics from NVIDIA's NCA-AMAD exam outline.
Two NCA-AMAD-style questions
A platform team deploys the same LLM NIM container image to two Kubernetes node pools, one with H100 GPUs and one with L40S GPUs. Nobody sets a model profile. What happens when each pod starts?
Each model-specific NIM image ships a manifest of pre-validated profiles that differ by engine, precision and parallelism. Without NIM_MODEL_PROFILE, NIM selects a compatible profile for the detected hardware, and its memory-aware selector filters out profiles that would not fit in GPU memory. Running list-model-profiles inside the container shows which profiles are compatible with a node. The same image serves both node pools, with no per-GPU image to download and no fixed profile that breaks on the smaller GPU.
A robotics company is planning its physical AI workflow and asks where each stage should run. Which mapping matches NVIDIA's three-computer pattern?
NVIDIA describes three computers for physical AI: DGX systems to train models, Omniverse and Cosmos on RTX PRO servers to simulate and generate synthetic data, and Jetson AGX Thor on the robot to run the trained model. The robot's computer is built for onboard inference, so training on it would be slow, and sending every sensor reading to a data center for inference adds network delay to decisions the robot has to make in milliseconds.
Full NCA-AMAD set: 6 timed exams, every answer explained, $19.99 one-time.
See the practice testsIf you picked a distractor, look at why it looked right. On NCA-AMAD that usually means you knew the product name but not the job it does, or where in the application's lifecycle it belongs.
Try 15 NCA-AMAD practice questions free
They come from the NCA-AMAD practice tests, and each option shows its explanation once you answer.
- 6 timed practice exams
- Every answer explained
- A score for each domain
- $19.99 one-time
What to look for in an NCA-AMAD practice test
Questions from all eight sections, in proportion. NVIDIA publishes the weight of each section on the certification page, from Agentic AI and Data Processing and Data Flywheel at 17% each down to Physical AI at 6%. A practice test that draws mostly on agents and RAG leaves you unprepared for the inference, sizing and NVIDIA AI Enterprise questions, which together make up more than a third of the exam.
An explanation for every option. Read the explanations for the questions you got right as well. Some of those were guesses, and the explanation is where you learn the reason.
The real pace. One hour for up to 60 questions leaves about a minute each. Timed practice shows whether you recognize answers or work them out slowly.
Free questions before you pay. A practice test worth buying lets you see its questions first, so you can judge the explanations yourself.
NCA-AMAD practice options in October 2026
We list NVIDIA's own resources first, because NVIDIA writes the exam, then the paid options, then marketplaces.
| Option | What you get | What it leaves out |
|---|---|---|
| NVIDIA's NCA-AMAD sample questions | Sample questions for every section on the certification page, in a Learning Mode that shows the answer, an explanation and a link to read more. Free. | A timed, full-length exam |
| NVIDIA's AI Model and Application Development Fundamentals course | The training NVIDIA recommends for the exam: an eight-hour, instructor-led workshop with hands-on work across the lifecycle, priced on request (course page) | Practice questions |
| preporato.com NCA-AMAD practice tests | Six timed tests of 60 questions with 60 minutes each, built to NVIDIA's section weights, an explanation for every option, a score for each section, and free sample questions before you buy. With Pro, hands-on labs for agents, RAG, fine-tuning, LLM serving, data and monitoring | A video course and a lab for Physical AI. NVIDIA does not describe the exam's hands-on lab, so the labs follow the topic list. Each test also has six Select TWO questions, while NVIDIA lists the exam as multiple choice and its sample questions are all single answer |
| Practice-test courses on Udemy and other marketplaces | None for NCA-AMAD when we checked Udemy, Whizlabs, SkillCertPro and CertKraken on October 20, 2026 | Check the preview questions and the date of the last update when sets appear |
We build the preporato.com tests, so weigh what we say about them accordingly. You can check the question style before paying: the free questions on the certificate page come from the same practice tests.
Hands-on practice for NCA-AMAD
NVIDIA's exam details list a hands-on lab, and as of October 2026 they do not say what it contains. NVIDIA's recommended course is built around hands-on work across the application lifecycle, from data processing and customization to inference and deployment.
Preporato Pro adds hands-on labs for the skills in NVIDIA's topic list: agents with tools, MCP, memory and NeMo Guardrails; RAG with chunking, hybrid search and reranking; LoRA, QLoRA and DPO fine-tuning; LLM serving with vLLM, batching and quantization, with load tests; data curation and PII redaction; and tracing and GPU monitoring. They run in the browser on hosted NVIDIA NIM endpoints, NVIDIA GPU pods and Kubernetes, with an automatic check at every step. Physical AI has no lab yet. When we checked Udemy, Whizlabs, SkillCertPro and CertKraken on October 20, 2026, none of them listed NCA-AMAD practice tests or labs.
Six labs to start with
One lab each for agents, RAG, fine-tuning, serving, data and tracing. The first two are free with an account; the rest come with Pro.
Build an Agent From Scratch: The Tool Loop Without a Framework
Hand-roll a tool-using agent on the chat completions API and nothing else: parse the model's tool calls, dispatch them to plain Python functions, close the loop with correctly threaded tool results, add stop conditions and budgets, turn every failure into a tool result the model can read, put an approval guardrail in front of a side-effecting refund tool, control context with truncation and a trace, and finish with an evaluation run that scores the agent on a fixed question set.
Build a RAG Pipeline with NVIDIA NIM
Build a complete Retrieval Augmented Generation pipeline — from document chunking to vector search to an agent that answers questions from your knowledge base.
Fine-Tune an LLM with LoRA and QLoRA (Jupyter)
Fine-tune Meta Llama 3 8B on a custom instruction dataset using LoRA and QLoRA. Learn parameter-efficient fine-tuning from data preparation through evaluation — the #1 most demanded AI skill.
vLLM Production Serving: PagedAttention, Continuous Batching, Prefix Caching
Stand up vLLM and measure the three features that make it the de-facto inference server: PagedAttention's KV-cache capacity, continuous batching throughput, and prefix caching speedups. Then write the production spec — server args, Kubernetes deployment, monitoring, autoscaling.
PII Detection and Redaction: Checksums, LLM Entity Extraction, Reversible Placeholders and a Leak Test
Build the redactor that stands between customer chats and an external LLM service. Find emails, phones, card numbers and IBANs with patterns validated by Luhn and mod-97, find names and addresses with an in-house model, merge and measure span coverage on labelled chats, replace PII with consistent typed placeholders you can restore, and prove with a leak test on held-out chats that nothing gets through.
LLM Observability: Trace, Cost and Debug a RAG Assistant
Instrument a real RAG assistant with traces from scratch: nested spans around retrieval and generation, token usage and cost attributed to every call, a latency and cost report, a triage tool that finds retrieval misses from trace evidence, PII redaction before anything is written, sampling that keeps every error and slow request, a cost budget alarm, and a CI gate that blocks a prompt change that makes the assistant slower or dearer.
Using practice tests in the last two weeks
Answer NVIDIA's sample questions first, in Learning Mode, to see the style the exam uses. Then take your first timed test cold and open the results page to see your misses grouped by section.
Spend the next three or four days on the section with the most misses. Go back to NVIDIA's topic list and suggested reading for that section, then reread the explanations for every question you missed in it.
Take a second test and compare the section scores. Often the weak section improves while another slips, because one test samples each section with only a few questions. Study the one that slipped next.
Keep that rhythm (a test, a few days on the weakest section, another test) until two tests you have not taken before both come back at 80% or higher. We use 80% as the line because NVIDIA scores the exam pass/fail and does not publish a passing score (as of October 2026), and a margin protects you from a hard draw of questions on the day. Then book the exam.
For a longer runway, the 4-week NCA-AMAD study plan lays out a month of preparation, and the 20 explained practice questions are a free warm-up before your first full test.
Frequently asked questions
Does NVIDIA offer official NCA-AMAD practice questions?
Yes. As of October 2026, the NCA-AMAD certification page offers sample questions for each section in a Learning Mode with answers, explanations and links. NVIDIA does not list a full-length practice exam.
How many NCA-AMAD practice tests should I take before the exam?
Take them until two tests you have not seen before both come back at 80% or higher. Plan for three or four tests with study in between, and let the scores tell you when to stop.
Are the preporato.com NCA-AMAD practice tests like the real exam?
They follow NVIDIA's eight sections and weights, and each test gives you 60 questions in 60 minutes, the top of the real exam's 50 to 60 questions in one hour. Two things differ. Each of our tests includes six Select TWO questions, while NVIDIA lists the exam as multiple choice. And NVIDIA's exam details list a hands-on lab without describing it, so our labs (with Pro) practice the skills in the topic list and make no claim to match that lab.
Is there an NCA-AMAD course with practice tests?
NVIDIA's recommended training, AI Model and Application Development Fundamentals, is an instructor-led workshop without practice tests. When we checked on October 20, 2026, we found no NCA-AMAD courses or practice sets on Udemy, Whizlabs, SkillCertPro or CertKraken. Check those marketplaces again before you buy.
Are there hands-on labs for NCA-AMAD?
NVIDIA's recommended course is an instructor-led workshop with hands-on work, priced on request as of October 2026. Preporato Pro includes browser labs for agents, RAG, fine-tuning, LLM serving, data curation and monitoring, two of them free with an account. On October 20, 2026, we found no NCA-AMAD labs or practice tests on Udemy, Whizlabs, SkillCertPro or CertKraken.
Where can I try NCA-AMAD questions for free?
NVIDIA's sample questions are on the certification page. The certificate page on preporato.com has free NCA-AMAD practice questions with explanations, and the NCA-AMAD practice questions article walks through 20 more.
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