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NVIDIA Generative AI Multimodal Certification Exam — Information & Free Practice

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Exam Review

About NVIDIA Generative AI Multimodal Certification Exam

NVIDIA Generative AI Multimodal Certification Exam · US

Key exam facts

Provider
NVIDIA; Certiverse registration account
Format
English multiple-choice examination, online and remotely proctored
Exam questions
Total questions: 50–60.
Time limit
1 hour
Exam fee
$125

Topics covered

  • Core Machine Learning and AI Knowledge (20%)

    • Training stability in multimodal settings
    • Multimodal loss functions
    • Machine learning fundamentals
    • Nonsequential neural networks and residual connections
    • Statistical analyses of multimodal pipelines
    • Multimodal-specific transfer learning
    • Emerging multimodal trends and technologies
    • Energy-efficient and trustworthy multimodal model design, development and deployment
    • Prompt engineering principles
    • Deep learning frameworks

  • Data Analysis (10%)

    • Dataset insights and data mining
    • Attention maps in multimodal settings
    • Visualization of analysis results
    • Relationships, trends and factors affecting research

  • Experimentation (25%)

    • Developing and testing multimodal AI models
    • Managing and preprocessing data from varied sources
    • Multimodal model explainability
    • Multimodal data quality and consistency
    • Testing model accuracy and effectiveness

  • Multimodal Data (15%)

    • Supervised deployment and scalability, performance and reliability evaluation
    • LLM use cases
    • Python natural language package capabilities
    • System data, hardware and software requirements
    • Monitoring data collection, experiments and software processes
    • Python packages for traditional machine learning analyses
    • Supervised software components and scripts

  • Performance Optimization (10%)

    • Computational efficiency and output accuracy
    • Model performance and hyperparameter tuning
    • Multimodal-specific transfer learning
    • Supervised model training and training optimization

  • Software Development (15%)

    • Client collaboration through requirements, data, reporting, deployment and integration
    • Software best practices, quality and reliability
    • Prompt engineering for generative models
    • U-Net image generation and autoencoders
    • CLIP text-to-image generation and diffusion-model training

  • Trustworthy AI (5%)

    • Ethical principles of trustworthy AI
    • Data privacy and consent
    • Technologies for AI trustworthiness
    • Minimizing bias

How to prepare

Work through the topics, read the explanation after each answer, and revisit the areas where you miss questions. Use the official outline and reference materials below to guide your study.

These independent practice questions are not actual exam items and do not guarantee a passing result.

Frequently asked questions

What is the exam format?

English multiple-choice examination, online and remotely proctored

How many questions are on the exam?

Total questions: 50–60.

How long is the exam?

1 hour

How much does the exam cost?

$125

Official sources and references

Confirm current exam arrangements with the exam provider.