NCA-AMAD
Free NVIDIA-Certified Associate: AI Model & Application Development Practice Test
12 exam-style questions with full explanations — no sign-up. Score yourself, then close your gaps with the full course.
Exam fee ~$1258 exam domainsLevel Beginner2 timed practice tests in the course
✅ Free practice — no sign-up📝 Real exam-style questions💡 Detailed explanations💸 30-day money-back via Udemy
A travel booking application must: query live flight availability from an external API, compare prices across three providers, and generate a personalized itinerary summary. Which approach BEST meets these requirements?
This requires live external data retrieval, comparison across multiple sources, and reasoning before synthesis — the exact scenario where an agentic workflow with tool calling is needed instead of a single model call.
A company deploys a customer support chatbot powered by a fine-tuned LLM. They want to continuously improve the model using real user interactions over time. Which approach BEST describes implementing a 'data flywheel'?
The data flywheel concept is defined by a continuous loop: production usage generates data, that data is curated and used to improve the model, and the improved model generates better production data — compounding value over time.
A company is building a real-time voice assistant that must return responses within 200ms per request on a GPU-based inference server. Which serving strategy BEST meets this requirement?
Dynamic batching with a small max batch size and short delay allows some throughput benefit from batching while keeping per-request latency low enough to meet the 200ms SLA.
A legal-tech company's internal policy documents change weekly, and their chatbot must always cite the latest version without constant retraining cycles. Which customization approach BEST meets this requirement?
RAG decouples knowledge freshness from model weights by retrieving current documents at query time, so updating the index (not the model) keeps answers current with near-zero retraining cost.
A company wants to build a document search system that returns results based on semantic meaning rather than exact keyword matches, by converting text passages into dense vector representations for similarity comparison in a vector database. Which model type BEST meets this requirement?
Embedding models are purpose-built to convert text into dense vector representations that capture semantic meaning, enabling similarity search (e.g., cosine similarity) against a vector database — exactly what semantic search requires.
A company wants to deploy a pretrained large language model into production with minimal customization, requiring an optimized, scalable inference API that can run consistently across cloud, data center, and workstation environments. Which NVIDIA AI Enterprise component BEST meets this requirement?
NIM microservices are prebuilt, containerized inference endpoints (built on Triton/TensorRT-LLM) that expose a standard API and run consistently across cloud, data center, and workstation with minimal setup — exactly the 'minimal customization, portable, optimized inference' use case.
A team needs to deploy a 13B-parameter LLM for inference using FP16 precision. Accounting for a typical 20% overhead for KV cache and activation memory, what is the MINIMUM GPU memory capacity they should provision from standard available options?
13B params x 2 bytes (FP16) = 26 GB for weights alone. Adding ~20% overhead for KV cache and activations brings the requirement to roughly 31 GB, which exceeds 24 GB but fits comfortably within a 40 GB GPU.
A robotics startup is developing a warehouse navigation robot. Before deploying to physical hardware, the team wants to train the robot's navigation policy in a photorealistic virtual environment, generate synthetic sensor data with domain randomization, and validate behavior safely at scale. Which NVIDIA platform BEST meets these requirements?
Isaac Sim is NVIDIA's robotics simulation application, built on Omniverse, purpose-built for training and testing robot policies in photorealistic, physically accurate virtual environments, including synthetic data generation with domain randomization for sim-to-real transfer.
A development team is building a customer-facing agent and must ensure the model cannot be jailbroken into discussing competitor pricing or generating harmful content. Which NVIDIA offering is purpose-built to enforce these conversational boundaries?
NeMo Guardrails provides programmable rails (topical, safety, jailbreak) specifically designed to constrain conversational AI behavior.
A development team is building a retrieval-augmented generation (RAG) pipeline and needs to convert document chunks into dense vector representations for similarity search. Which type of NVIDIA component should they use?
Embedding microservices packaged as NIMs are purpose-built to take text input and return dense vector embeddings, which is exactly what's needed to populate a vector store for RAG retrieval.
A team needs to deploy a fine-tuned LLM in production with an industry-standard inference API, an optimized runtime engine, and minimal custom integration work. Which NVIDIA offering BEST fits this need?
NVIDIA NIM packages an optimized inference engine with a standardized, OpenAI-compatible API and container-based deployment, minimizing custom integration effort.
A startup wants its LLM to consistently adopt a specific brand tone and structured output format. They have a small labeled dataset of 2,000 examples and only a single GPU with 24GB VRAM. Which technique is MOST appropriate?
LoRA/PEFT freezes the base model and trains small low-rank adapter matrices, drastically reducing GPU memory and compute needs while still durably encoding style/format behavior from a modest dataset — ideal for single-GPU budgets.
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How much does the NCA-AMAD exam cost?
The exam fee is approximately $125 and varies by region — confirm current pricing with the certification vendor before you book.
What topics are on the exam?
It covers 8 domains: Agentic AI (17%), Data Processing and the 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%), Physical AI (6%). The full course has a dedicated chapter, lab and practice-test coverage for each.
Is this practice test really free?
Yes — all questions on this page are free with explanations and no sign-up. The paid Udemy course adds two full-length timed exams, video lessons and hands-on labs.
Will this prepare me for the real exam?
The questions mirror the real exam's style and are mapped to the official domains. This is exam-focused preparation — combine the free test with the full course's timed simulations to gauge your readiness.