NCA-GENL study guide · 2026

The 5 NCA-GENL domains, explained

5 domains and 33 official objectives. Core Machine Learning and AI Knowledge carries the most weight at 30%. Each guide explains what the domain covers, how its questions are framed and where candidates lose marks.

Start with Domain 1 See all domains
Questions
50-60 multiple-choice questions
Time
60 minutes
Passing score
Not published
Outline
NCA-GENL exam study guide (Jan 2025 edition) and nvidia.com exam page (current)

Exam blueprint

Bar widths follow the official weights. Weight ranges are drawn at their midpoint.

  1. 1 30%
  2. 2 14%
  3. 3 22%
  4. 4 24%
  5. 5 10%
  1. 1 Core Machine Learning and AI Knowledge
  2. 2 Data Analysis
  3. 3 Experimentation
  4. 4 Software Development
  5. 5 Trustworthy AI

All 5 domains

33 official objectives 63–85 hrs suggested study

  1. Domain 01 30%

    Core Machine Learning and AI Knowledge

    The largest NCA-GENL domain: how transformer LLMs work, classic machine learning fundamentals, text embeddings, prompt engineering, and building RAG pipelines, chatbots and summarizers with Python tools.

    Inside this domain

    • Assist in deployment and evaluation of model scalability, performance, and reliability under the supervision of senior team members.
    • Awareness of the process of extracting insights from large datasets using data mining, data visualization, and similar techniques.
    • Build LLM use cases such as retrieval-augmented generation (RAG), chatbots, and summarizers.
    • Curate and embed content datasets for RAGs.
    • and 6 more objectives

    Watch for Fine-tune for fresh facts

    Objectives
    10
    Questions est.
    ~17
    Study time est.
    20–25 hrs
  2. Domain 02 14%

    Data Analysis

    This domain tests how you explore large datasets, compare models with statistical metrics such as loss and explained variance, chart results clearly, and spot factors that distort findings.

    Inside this domain

    • Awareness of the process of extracting insights from large datasets using data mining, data visualization, and similar techniques (also objective 1.2)
    • Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
    • Conduct data analysis under the supervision of a senior team member.
    • Create graphs, charts, or other visualizations to convey the results of data analysis using specialized software.
    • and 1 more objectives

    Watch for Higher perplexity wins

    Objectives
    5
    Questions est.
    ~8
    Study time est.
    8–12 hrs
  3. Domain 03 22%

    Experimentation

    Experimentation covers training and fine-tuning LLMs, preparing pretraining and alignment data, running hardware and software tests, comparing technologies, RLHF data collection, and benchmarking models and pipelines.

    Inside this domain

    • Assist in model training and training optimization under the supervision of a senior team member.
    • Assist in preparing (e.g., scraping, tokenization) large datasets for pretraining, fine-tuning, and RLHF.
    • Assist in the design and conduct of hardware or software tests for LLM applications.
    • Assist in the evaluation of current or emerging technologies to consider factors such as cost, portability, compatibility, or usability.
    • and 3 more objectives

    Watch for Full fine-tune on one GPU

    Objectives
    7
    Questions est.
    ~12
    Study time est.
    15–20 hrs
  4. Domain 04 24%

    Software Development

    Software Development covers building and deploying LLM applications: serving with Triton, TensorRT-LLM and NIM, sizing GPUs, writing Python components, and monitoring data and experiment pipelines.

    Inside this domain

    • Assist in the deployment and evaluations of model scalability, performance, and reliability under the supervision of a senior team member.
    • Build LLM use cases such as RAGs, chatbots, and summarizers.
    • Familiarity with the capabilities of Python natural language packages (spaCy, NumPy, vector databases, etc.) (also objective 1.6)
    • Identify system data, hardware, or software components required to meet user needs.
    • and 3 more objectives

    Watch for NeMo for serving

    Objectives
    7
    Questions est.
    ~13
    Study time est.
    15–20 hrs
  5. Domain 05 10%

    Trustworthy AI

    Trustworthy AI covers the ethical principles behind responsible LLMs, balancing data privacy with consent, NVIDIA tools such as NeMo Guardrails that improve safety, and methods for reducing bias.

    Inside this domain

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

    Watch for Anonymized means unrestricted

    Objectives
    4
    Questions est.
    ~6
    Study time est.
    5–8 hrs

Study the heaviest domains first

Marks follow weight. This order puts the most marks behind you first, across about 63–85 hrs of study.

  1. 1 Core Machine Learning and AI Knowledge 30% · 20–25 hrs
  2. 4 Software Development 24% · 15–20 hrs
  3. 3 Experimentation 22% · 15–20 hrs
  4. 2 Data Analysis 14% · 8–12 hrs
  5. 5 Trustworthy AI 10% · 5–8 hrs

Try one

Practice questionDomain 1: Core Machine Learning and AI Knowledge

A support team wants an assistant that answers questions from its product manuals. The manuals change every few weeks, and each answer must cite the section it came from. Which approach best meets these needs?

One trap from every domain

The answer that looks right, and the clue that gives it away.

  • Domain 1

    Fine-tune for fresh facts

    Clue The scenario mentions frequently updated documents or a need for citations.

  • Domain 2

    Higher perplexity wins

    Clue The table reports perplexity or loss.

  • Domain 3

    Full fine-tune on one GPU

    Clue The scenario stresses limited GPU memory or cost.

  • Domain 4

    NeMo for serving

    Clue The requirement is low-latency production inference.

  • Domain 5

    Anonymized means unrestricted

    Clue The scenario introduces a new purpose for existing data.

Other Artificial Intelligence & Machine Learning certifications

Study guides for exams that overlap with NCA-GENL.

Practise what you read

NCA-GENL domains: common questions

How many domains are in the NCA-GENL exam?

NCA-GENL has 5 domains with 33 official objectives between them: Core Machine Learning and AI Knowledge, Data Analysis, Experimentation, Software Development and Trustworthy AI.

Which NCA-GENL domain carries the most weight?

Core Machine Learning and AI Knowledge is the heaviest at 30%, followed by Software Development (24%). The lightest is Trustworthy AI (10%). Weights come from NVIDIA's official outline.

What is the NCA-GENL exam format?

Questions: 50-60 multiple-choice questions. Time allowed: 60 minutes. Passing score: Not published.

How long should I study for NCA-GENL?

Our planning estimate is 63–85 hrs across all domains, split by weight. It is an editorial estimate, not an official figure, so adjust it to your experience and to how you score on practice questions.

Where should I start?

Start with Core Machine Learning and AI Knowledge, because it carries the most marks, then work down by weight. Use the checklist on each domain page to confirm you can do what the domain asks before moving on.

Which outline is this guide based on?

This guide follows NCA-GENL exam study guide (Jan 2025 edition) and nvidia.com exam page (current), published by NVIDIA. It was last reviewed in October 2026.

Domain names, weights and objectives come from NVIDIA's official outline (NCA-GENL exam study guide (Jan 2025 edition) and nvidia.com exam page (current)). Question counts and study hours are editorial planning estimates, not official figures. Last reviewed October 2026.