NCA-GENM study guide · 2026

The 7 NCA-GENM domains, explained

7 domains and 39 official objectives. Experimentation carries the most weight at 25%. Each guide explains what the domain covers, how its questions are framed and where candidates lose marks.

Start with Domain 3 See all domains
Questions
50-60 multiple-choice questions
Time
60 minutes
Passing score
Not published
Outline
NCA-GENM Exam Study Guide (NVIDIA, Apr 2025 edition, current)

Exam blueprint

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

  1. 1 20%
  2. 2 10%
  3. 3 25%
  4. 4 15%
  5. 5 10%
  6. 6 15%
  7. 7 5%
  1. 1 Core Machine Learning and AI Knowledge
  2. 2 Data Analysis
  3. 3 Experimentation
  4. 4 Multimodal Data
  5. 5 Performance Optimization
  6. 6 Software Development
  7. 7 Trustworthy AI

All 7 domains

39 official objectives 57–83 hrs suggested study

  1. Domain 01 20%

    Core Machine Learning and AI Knowledge

    The machine learning and deep learning foundations behind multimodal generative models: training stability, contrastive and reconstruction losses, residual networks, evaluation statistics, transfer learning, prompting and the frameworks used to build them.

    Inside this domain

    • Control stability of training in multimodal settings
    • Develop content for introduction to multimodal loss functions
    • Familiarity with fundamentals of machine learning (e.g., feature engineering, model comparison, cross validation)
    • Understand nonsequential neural networks and residual connections
    • and 6 more objectives

    Watch for Cross-entropy aligns modalities

    Objectives
    10
    Questions est.
    ~11
    Study time est.
    12–16 hrs
  2. Domain 02 10%

    Data Analysis

    How to pull insight from large multimodal datasets, read attention maps that show where a model looks, pick the right chart for a result, and spot trends or confounders that could distort conclusions.

    Inside this domain

    • Awareness of the process of extracting insights from large datasets using data mining, data visualization, and similar techniques
    • Develop content for attention maps in multimodal settings
    • Create graphs, charts, or other visualizations to convey the results of data analysis using specialized software
    • Identify relationships and trends or any factors that could affect the results of research

    Watch for Attention is explanation

    Objectives
    4
    Questions est.
    ~5
    Study time est.
    5–8 hrs
  3. Domain 03 25%

    Experimentation

    The largest NCA-GENM domain: building and testing multimodal models, preprocessing image, text and audio data, using models to explain predictions, checking data quality across modalities and measuring whether outputs are accurate.

    Inside this domain

    • Assist in developing and testing multimodal AI models
    • Manage and preprocess data from various sources
    • Use multimodal models to improve explainability
    • Test data quality and consistency in a multimodal setting
    • and 1 more objectives

    Watch for Any augmentation is safe

    Objectives
    5
    Questions est.
    ~14
    Study time est.
    14–20 hrs
  4. Domain 04 15%

    Multimodal Data

    Working with text, images, audio and other data types together: fusing them, handling gaps, building retrieval and chatbot use cases over mixed content, choosing Python packages and infrastructure, and monitoring data pipelines.

    Inside this domain

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

    Watch for Early fusion always wins

    Objectives
    7
    Questions est.
    ~8
    Study time est.
    8–12 hrs
  5. Domain 05 10%

    Performance Optimization

    Making multimodal models faster, cheaper and more accurate: mixed precision, quantization, pruning, TensorRT, hyperparameter tuning, transfer learning and training efficiency on NVIDIA GPUs.

    Inside this domain

    • Enhance computational efficiency and improve the accuracy of outputs in AI models
    • Optimize the performance of AI models, including tuning hyperparameters
    • Develop content for multimodal-specific transfer learning
    • Assist in model training and training optimization under the supervision of a senior team member

    Watch for Smaller batch fixes memory

    Objectives
    4
    Questions est.
    ~5
    Study time est.
    6–9 hrs
  6. Domain 06 15%

    Software Development

    Building multimodal generative applications: U-Net denoisers and diffusion, CLIP-guided text-to-image generation, prompt engineering, client collaboration, software quality, and NVIDIA SDKs such as NeMo, Riva, Triton and ACE.

    Inside this domain

    • Collaborate with the client during requirements acquisition, data gathering, progress reporting, deployment, and integration
    • Ensure adherence to best practices and maintain high standards of software quality and reliability
    • Use prompt engineering to better influence the output of generative AI models
    • Build a U-Net to generate images from pure noise and as a type of autoencoder
    • and 1 more objectives

    Watch for CLIP draws the picture

    Objectives
    5
    Questions est.
    ~8
    Study time est.
    9–13 hrs
  7. Domain 07 5%

    Trustworthy AI

    The ethics and safeguards around multimodal generative AI: trustworthy AI principles, privacy and consent for images and voices, tools that make systems safer and more transparent, and ways to reduce 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 Public means permitted

    Objectives
    4
    Questions est.
    ~3
    Study time est.
    3–5 hrs

Study the heaviest domains first

Marks follow weight. This order puts the most marks behind you first, across about 57–83 hrs of study.

  1. 3 Experimentation 25% · 14–20 hrs
  2. 1 Core Machine Learning and AI Knowledge 20% · 12–16 hrs
  3. 4 Multimodal Data 15% · 8–12 hrs
  4. 6 Software Development 15% · 9–13 hrs
  5. 2 Data Analysis 10% · 5–8 hrs
  6. 5 Performance Optimization 10% · 6–9 hrs
  7. 7 Trustworthy AI 5% · 3–5 hrs

Try one

Practice questionDomain 3: Experimentation

A team generates product images from text prompts with a diffusion model. Reviewers say the images look realistic but often show the wrong product color or count. Which metric best tracks this specific problem across model versions?

One trap from every domain

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

  • Domain 1

    Cross-entropy aligns modalities

    Clue The goal mentions matching pairs or a shared embedding space.

  • Domain 2

    Attention is explanation

    Clue The option uses words like proves or guarantees.

  • Domain 3

    Any augmentation is safe

    Clue Captions describe spatial positions or colors.

  • Domain 4

    Early fusion always wins

    Clue Some records lack audio or images.

  • Domain 5

    Smaller batch fixes memory

    Clue The model relies on large batches, such as CLIP-style training.

  • Domain 6

    CLIP draws the picture

    Clue The question asks which component creates the image.

  • Domain 7

    Public means permitted

    Clue The data involves identifiable people.

Other Artificial Intelligence & Machine Learning certifications

Study guides for exams that overlap with NCA-GENM.

Practise what you read

NCA-GENM domains: common questions

How many domains are in the NCA-GENM exam?

NCA-GENM has 7 domains with 39 official objectives between them: Core Machine Learning and AI Knowledge, Data Analysis, Experimentation, Multimodal Data, Performance Optimization, Software Development and Trustworthy AI.

Which NCA-GENM domain carries the most weight?

Experimentation is the heaviest at 25%, followed by Core Machine Learning and AI Knowledge (20%). The lightest is Trustworthy AI (5%). Weights come from NVIDIA's official outline.

What is the NCA-GENM exam format?

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

How long should I study for NCA-GENM?

Our planning estimate is 57–83 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 Experimentation, 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-GENM Exam Study Guide (NVIDIA, Apr 2025 edition, current), published by NVIDIA. It was last reviewed in October 2026.

Domain names, weights and objectives come from NVIDIA's official outline (NCA-GENM Exam Study Guide (NVIDIA, Apr 2025 edition, current)). Question counts and study hours are editorial planning estimates, not official figures. Last reviewed October 2026.