NCA-GENL · Associate · 60 min · 50-60 questions · $125

Generative AI LLMs

Full course Full course: 111 original questions with NVIDIA-sourced explanations, a learning guide for every official objective, 8 in-browser labs, a glossary and a timed mock exam.

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Readiness by domain

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Mock exam

50 questions · 60 minutes · weighted like the real exam.

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Exam details and more tools

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Practice options: choose a domain, length, or drill only missed or bookmarked questions (111 questions).
Full dashboard: streak, mock history, missed and bookmarked lists, export or import your progress.
Learning guides: one short guide per official objective, every point backed by an NVIDIA quote.
Hands-on labs: 8 in-browser labs (tokenizer, sampling, RAG and more). No sign-up, no API calls.
Glossary: 82 official terms in plain language.
Ask Aegis: the tutor button at the bottom right answers from the course content with citations.
Xid error reference: every in-use NVIDIA Xid code with NVIDIA's recommended actions.
HGX hardware guide: a beginner's tour of an 8-GPU server.

Exam facts

Level: Associate
Duration: 1 hour
Questions: 50-60 multiple-choice
Price: $125
Delivery: Online, remotely proctored
Language: English
Validity: Two years from issuance
Scoring: Pass/fail (no numeric score is reported)
Prerequisites: A basic understanding of generative AI and large language models

Exam facts verified against NVIDIA's NCA-GENL page on 2026-10-08. NVIDIA's page is internally inconsistent on question count: summary says 50 questions, exam details say "50-60 multiple-choice". Always confirm on nvidia.com before booking.

Official blueprint

Core Machine Learning and AI Knowledge — 30%

1.1 Assist in deployment and evaluation of model scalability, performance, and reliability under the supervision of senior team members.
1.2 Awareness of the process of extracting insights from large datasets using data mining, data visualization, and similar techniques.
1.3 Build LLM use cases such as retrieval-augmented generation (RAG), chatbots, and summarizers.
1.4 Curate and embed content datasets for RAGs.
1.5 Familiarity with the fundamentals of machine learning (e.g., feature engineering, model comparison, cross validation).
1.6 Familiarity with the capabilities of Python natural language packages (spaCy, NumPy, vector databases, etc.).
1.7 Read research papers (articles, conference papers, etc.) to identify emerging LLM trends and technologies.
1.8 Select and use models to create text embeddings.
1.9 Use prompt engineering principles to create prompts to achieve desired results.
1.10 Use Python packages (spaCy, NumPy, Keras, etc.) to implement specific traditional machine learning analyses.

Software Development — 24%

2.1 Assist in the deployment and evaluations of model scalability, performance, and reliability under the supervision of a senior team member.
2.2 Build LLM use cases such as RAGs, chatbots, and summarizers.
2.3 Familiarity with the capabilities of Python natural language packages (spaCy, NumPy, vector databases, etc.).
2.4 Identify system data, hardware, or software components required to meet user needs.
2.5 Monitor functioning of data collection, experiments, and other software processes.
2.6 Use Python packages (spaCy, NumPy, Keras, etc.) to implement specific traditional machine learning analyses.
2.7 Write software components or scripts under the supervision of a senior team member.

Experimentation — 22%

3.1 Assist in model training and training optimization under the supervision of a senior team member.
3.2 Assist in preparing (e.g., scraping, tokenization) large datasets for pretraining, fine-tuning, and RLHF.
3.3 Assist in the design and conduct of hardware or software tests for LLM applications.
3.4 Assist in the evaluation of current or emerging technologies to consider factors such as cost, portability, compatibility, or usability.
3.5 Awareness of, and/or participation in, data collection from human subjects (e.g., RLHF).
3.6 Evaluate and refine existing models / benchmarking.
3.7 Executes experimentation to evaluate models and pipelines.

Data Analysis and Visualization — 14%

4.1 Awareness of the process of extracting insights from large datasets using data mining, data visualization, and similar techniques.
4.2 Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
4.3 Conduct data analysis under the supervision of a senior team member.
4.4 Create graphs, charts, or other visualizations to convey the results of data analysis using specialized software.
4.5 Identify relationships and trends or any factors that could affect the results of research.

Trustworthy AI — 10%

5.1 Describe the ethical principles of trustworthy AI.
5.2 Describe the balance between data privacy and the importance of data consent.
5.3 Describe how to use NVIDIA and other technologies to improve AI trustworthiness.
5.4 Describe how to minimize bias in AI systems.

Coverage of every official objective

Coverage of official objectives
Obj.	Official objective	Guide	Practice Qs	Labs	Glossary terms
1.1	Assist in deployment and evaluation of model scalability, performance, and reliability under the supervision of senior team members.	Guide	3	KV cache lab	3
1.2	Awareness of the process of extracting insights from large datasets using data mining, data visualization, and similar techniques.	Guide	3	—	1
1.3	Build LLM use cases such as retrieval-augmented generation (RAG), chatbots, and summarizers.	Guide	4	RAG lab	3
1.4	Curate and embed content datasets for RAGs.	Guide	3	Chunking lab, RAG lab	3
1.5	Familiarity with the fundamentals of machine learning (e.g., feature engineering, model comparison, cross validation).	Guide	4	—	4
1.6	Familiarity with the capabilities of Python natural language packages (spaCy, NumPy, vector databases, etc.).	Guide	3	—	4
1.7	Read research papers (articles, conference papers, etc.) to identify emerging LLM trends and technologies.	Guide	3	—	4
1.8	Select and use models to create text embeddings.	Guide	3	RAG lab	2
1.9	Use prompt engineering principles to create prompts to achieve desired results.	Guide	4	Sampling lab	6
1.10	Use Python packages (spaCy, NumPy, Keras, etc.) to implement specific traditional machine learning analyses.	Guide	3	—	2
2.1	Assist in the deployment and evaluations of model scalability, performance, and reliability under the supervision of a senior team member.	Guide	3	KV cache lab	1
2.2	Build LLM use cases such as RAGs, chatbots, and summarizers.	Guide	4	—	5
2.3	Familiarity with the capabilities of Python natural language packages (spaCy, NumPy, vector databases, etc.).	Guide	3	—	3
2.4	Identify system data, hardware, or software components required to meet user needs.	Guide	5	—	5
2.5	Monitor functioning of data collection, experiments, and other software processes.	Guide	4	—	2
2.6	Use Python packages (spaCy, NumPy, Keras, etc.) to implement specific traditional machine learning analyses.	Guide	3	—	4
2.7	Write software components or scripts under the supervision of a senior team member.	Guide	4	—	2
3.1	Assist in model training and training optimization under the supervision of a senior team member.	Guide	4	LoRA lab	7
3.2	Assist in preparing (e.g., scraping, tokenization) large datasets for pretraining, fine-tuning, and RLHF.	Guide	7	Tokenizer lab, Deduplication lab	7
3.3	Assist in the design and conduct of hardware or software tests for LLM applications.	Guide	2	—	3
3.4	Assist in the evaluation of current or emerging technologies to consider factors such as cost, portability, compatibility, or usability.	Guide	4	LoRA lab	6
3.5	Awareness of, and/or participation in, data collection from human subjects (e.g., RLHF).	Guide	2	—	3
3.6	Evaluate and refine existing models / benchmarking.	Guide	3	—	2
3.7	Executes experimentation to evaluate models and pipelines.	Guide	2	—	0
4.1	Awareness of the process of extracting insights from large datasets using data mining, data visualization, and similar techniques.	Guide	3	—	2
4.2	Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.	Guide	4	Metrics lab	3
4.3	Conduct data analysis under the supervision of a senior team member.	Guide	3	—	2
4.4	Create graphs, charts, or other visualizations to convey the results of data analysis using specialized software.	Guide	3	—	1
4.5	Identify relationships and trends or any factors that could affect the results of research.	Guide	3	—	0
5.1	Describe the ethical principles of trustworthy AI.	Guide	3	—	1
5.2	Describe the balance between data privacy and the importance of data consent.	Guide	3	—	2
5.3	Describe how to use NVIDIA and other technologies to improve AI trustworthiness.	Guide	4	—	4
5.4	Describe how to minimize bias in AI systems.	Guide	2	—	1

Every row is an official objective from NVIDIA's exam page. Every guide point and practice explanation cites a verbatim quote from an NVIDIA source; a CI check fails if any item lacks an objective tag or an NVIDIA source, or if question counts drift from the official domain weights.

Study roadmap

Baseline. Read the official exam page and study guide, then take a short practice set to see where you stand.
Core Machine Learning and AI Knowledge (30%). Work through its objectives, then drill its practice questions until readiness is above 80%.
Software Development (24%). Work through its objectives, then drill its practice questions until readiness is above 80%.
Experimentation (22%). Work through its objectives, then drill its practice questions until readiness is above 80%.
Data Analysis and Visualization (14%). Work through its objectives, then drill its practice questions until readiness is above 80%.
Trustworthy AI (10%). Work through its objectives, then drill its practice questions until readiness is above 80%.
Exam rehearsal. Take a full timed mock exam, review every miss, repeat until you score comfortably above your target.