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NVIDIA-Certified Associate Generative AI Multimodal — Requirements & Free Practice

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NVIDIA-Certified Associate Generative AI Multimodal

Licensing authority: NVIDIA · Application information

Initial by examination
NVIDIA Generative AI Multimodal Certification Exam
Applicability
Current official exam detail and linked program policy support the fixed evaluationDate. Neither an official start date nor examination retirement/end date is stated. Targeted official-domain searches disclosed no applicable retirement/replacement announcement, including none triggering the 2027-01-28 threshold or the before-2027-03-01 exclusion. The two-year holder validity and 60-day booking window are not version boundaries.
Award
Passing this examination yields the associate-level Multimodal Generative AI badge and optional certificate; personal certification validity is two years from issuance and recertification is by retaking the exam.
Delivery
English, online and remotely proctored, with a one-hour time limit. NVIDIA's linked policies require a secure browser, remote monitoring/recording, identity verification with a valid government-issued photo/signature ID matching the registered name, and a remote environment security assessment. Remote breaks are not allowed; no other devices or reference material may be used.
Eligibility
The exam page states a basic understanding of generative AI as its prerequisite. It presents an associate developer audience; it does not make an employment title a prerequisite.
Registration
A Certiverse account is required; the official Register for NCA-GENM Exam link is https://www.certiverse.com/#/checkout/nvidia/store-exam/NCA-GENM. The fee is $125. The checkout redirected to sign-in, so individual seat availability was not verified. No institution-arranged alternative or additional performance/writing assessment is established for this route by the current in-scope public sources.
Retakes
Failed attempts require a new purchase and a 14-day wait; at most five attempts are permitted in the 12-month period starting with the first purchase.
Scheduling
Appointments may be scheduled up to 60 days ahead. Cancellation and rescheduling must occur at least 24 hours before the session; a rescheduled exam must occur within two months of payment. A missed or taken exam is not refundable. The public offer does not prove inventory for every date in the requested horizon.
Training
The linked certification process and domain panels describe training and readings as recommended preparation; they do not establish mandatory course completion or a prerequisite certification examination.

NVIDIA Generative AI Multimodal Certification Exam

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
Examination outline
  • 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
Examination sources