NCA-GENL exam: format, domains and how to prepare
Who the NCA-GENL exam is for
NVIDIA's exam page presents the Generative AI LLMs certification (NCA-GENL) as "an entry-level credential" that validates the foundational concepts for developing, integrating and maintaining AI-driven applications using generative AI and large language models with NVIDIA solutions. The level is Associate, and the stated prerequisite is "a basic understanding of generative AI and large language models".
The page describes the target role as an associate developer who contributes to the development, programming and quality assurance of generative AI LLM systems, working in a team to develop datasets, select and train models, and test and debug them. Several topics are phrased "under the supervision of a senior team member": the exam expects someone who can take part in this work, not lead it.
Exam format
| Parameter | Value |
|---|---|
| Exam code | NCA-GENL |
| Level | Associate |
| Duration | 1 hour |
| Questions | 50-60, multiple choice |
| Language | English |
| Scoring | Pass/fail, no score given |
| Validity | Two years from issuance; renewed by retaking the exam |
Scoring comes from NVIDIA's certification FAQ, which states that its exams are pass/fail. The same FAQ sets a 14-day waiting period before a retake, a limit of five attempts at a given exam per 12 months, and no breaks during remotely proctored exams. The exam itself is accessed through a Certiverse account.
Domains and weights
The exam page lists five domains, each with its weight and its exam topics. The two most technical domains, core knowledge and software development, carry more than half of the exam.
| Domain | Weight | What it covers |
|---|---|---|
| Core Machine Learning and AI Knowledge | 30 % | ML fundamentals (feature engineering, model comparison, cross validation); LLM use cases such as RAG, chatbots and summarizers; curating and embedding content for RAG; text embeddings; prompt engineering; Python packages such as spaCy, NumPy and Keras; reading research papers |
| Data Analysis | 14 % | Extracting insights from large datasets; comparing models with statistical metrics such as loss functions or explained variance; charts and visualizations; spotting relationships and trends |
| Experimentation | 22 % | Model training and training optimization; preparing large datasets (scraping, tokenization) for pretraining, fine-tuning and RLHF; hardware and software tests for LLM applications; benchmarking and evaluating models and pipelines |
| Software Development | 24 % | Deploying and evaluating model scalability, performance and reliability; building RAG, chatbot and summarizer use cases; identifying the data, hardware or software components a need requires; monitoring data collection and experiments; writing scripts |
| Trustworthy AI | 10 % | Ethical principles of trustworthy AI; data privacy versus data consent; NVIDIA and other technologies that improve trustworthiness; minimizing bias |
Points worth noting
- Overlap: several topics appear in two domains, for example building LLM use cases and Python natural language packages (core knowledge and software development), or extracting insights from large datasets (core knowledge and data analysis). Studying them once covers both.
- Classic ML still counts: feature engineering, cross validation and traditional ML analyses with Python packages sit next to the LLM topics.
- RLHF: the Experimentation domain names reinforcement learning from human feedback, both for dataset preparation and for data collection from human subjects.
- Reading list: each domain comes with suggested readings, including "Attention Is All You Need", LoRA, the Hugging Face LLM course, the Triton Inference Server quick start, TensorRT and NeMo best practices.
How to prepare
- Read the NCA-GENL exam page on nvidia.com and copy every exam topic of the five domains into a checklist.
- Read the certification FAQ and exam policies before scheduling: retakes, breaks, online proctoring.
- Follow the two courses of the learning path shown on the exam page: "Fundamentals of Deep Learning" (open-source materials) and "Rapid Application Development with Large Language Models (LLMs)".
- Pick two or three suggested readings per domain rather than all of them, starting with the Core Machine Learning and AI Knowledge domain, which weighs the most.
- Build one small RAG pipeline end to end: chunk documents, create embeddings, store them in a vector database, query them with a prompt, then evaluate the answers on a few test questions. It touches topics from three domains at once.
- Review the classic ML and statistics topics separately: loss functions, explained variance, cross validation, feature engineering.
- Take our NCA-GENL practice exam under timed conditions, then review each missed question in the free study mode.
- Shortly before the exam, re-read the exam page in case the topics, the learning path or the conditions have changed.
Practice Exam NCA-GENL: Certified Associate: Generative AI with LLMs
25 free questions with explanations, no account needed.