NVIDIA-Certified Associate

Generative AI Multimodal

(NCA-GENM)

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About This Certification

The NCA Generative AI Multimodal certification is an entry-level credential that validates the foundational skills needed to design, implement, and manage AI systems that synthesize and interpret data across text, image, and audio modalities. The exam is online and proctored remotely, includes 50 questions, and has a 60-minute time limit.

Please carefully review NVIDIA's examination policy before scheduling your exam.

If you have any questions, please contact us here.

Certification Exam Details

Duration: 1 hour

Price: $125

Certification level: Associate

Subject: Multimodal generative AI

Number of questions: 50-60 multiple-choice

Prerequisites: A basic understanding of generative AI

Language: English

Validity: This certification is valid for two years from issuance.

Recertification may be achieved by retaking the exam.

Credentials: Upon passing the exam, participants will receive a digital badge and optional certificate indicating the certification level and topic.

Exam Preparation

Topics Covered in the Exam

Topics covered in the exam include:

  • Core machine learning and AI knowledge
  • Data analysis and visualization
  • Experimentation
  • Multimodal data
  • Performance optimization
  • Software development and engineering
  • Trustworthy AI

Candidate Audiences

  • AI DevOps engineers
  • AI strategists
  • Applied data research engineers
  • Applied data scientists
  • Applied deep learning research scientists
  • Cloud solution architects
  • Data scientists
  • Deep learning performance engineers
  • Generative AI specialists
  • Large language model (LLM) specialists and researchers
  • Machine learning engineers
  • Senior researchers
  • Software engineers
  • Solutions architects

Exam Study Guide

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Exam Blueprint

 Please review the table below. It’s organized by topic and weight to indicate how much of the exam is focused on each subject. Topics are mapped to NVIDIA Training courses and workshops that cover those subjects and that you can use to prepare for the exam.

Recommended Training
Type of course | Duration | Cost		 Content Breakdown 25%
Experimentation		 20%
Core Machine Learning and AI Knowledge		 15%
Multimodal Data		 15%
Software Development		 10%
Data Analysis and Visualization		 10%
Performance Optimization		 5%
Trustworthy AI

You can take one of these courses:
Getting Started With Deep Learning
Self-paced | 8 hours | $90
Fundamentals of Deep Learning
Workshop | 8 hours | $500
See Details

You can take one of these courses:
Introduction to Transformer-Based Natural Language Processing
Self-paced | 6 hours | $30
Building Transformer-Based Natural Language Processing Applications
Workshop | 8 hours | $500
See Details

Building Conversational​ AI Applications
Workshop | 8 hours | $500
See Details

You can take one of these courses:
Generative AI With Diffusion Models
Self-paced | 8 hours | $90
Generative AI With Diffusion Models
Workshop | 8 hours | $500
See Details

Building AI Agents with Multimodal Models
Workshop | 8 hours | $500
(Coming soon)
See Details

Review These Additional Materials

 

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You can take one of these courses:

Getting Started With Deep Learning
Fundamentals of Deep Learning

Skills covered in these courses:

Data Analysis ​

  • Enhance datasets through data augmentation to improve model accuracy.

Core Machine Learning and AI Knowledge​

  • Understand the fundamental techniques and tools required to train a deep learning model.
  • Gain experience with common deep learning data types and model architectures

Performance Optimization

  • Leverage transfer learning between models to achieve efficient results with less data and computation.

Software Development

  • Gain experience with common deep learning data types and model architectures.
  • Take on your own project with a modern deep learning framework.
See More Details for Getting Started With Deep Learning
See More Details for Fundamentals of Deep Learning

You can take one of these courses:

Introduction to Transformer-Based Natural Language Processing

Building Transformer-Based Natural Language Processing Applications

Skills covered in these courses:

Experimentation​

  • Understand how transformer-based LLMs can be used to manipulate, analyze, and generate text-based data​.
  • Leverage pretrained, modern LLMs to solve various natural language processing (NLP) tasks such as token classification, text classification, summarization, and question-answering.

Core Machine Learning and AI Knowledge​

  • Learn to describe how transformers are used as the basic building blocks of modern LLMs for NLP applications​.

Data Analysis

  • Understand how transformer-based LLMs can be used to manipulate, analyze, and generate text-based data.

Data Analysis​ and Visualization​

  • Understand how transformer-based LLMs can be used to manipulate, analyze, and generate text-based data.
See More Details for Introduction to Transformer-Based Natural Language Processing
See More Details for Building Transformer-Based Natural Language Processing Applications

Building Conversational​ AI Applications

Skills covered in this course:

Experimentation​

  • Customize and deploy automatic speech recognition (ASR) and test-to-speech (TTS) models on NVIDIA® Riva.​
  • Build and deploy an end-to-end conversational AI pipeline, including ASR, natural language processing (NLP), and TTS models, on Riva.​
  • Deploy a production-level conversational AI application with a Helm chart for scaling in Kubernetes clusters.

Multimodal Data​

  • Customize and deploy ASR and TTS models on Riva.​
  • Build and deploy an end-to-end conversational AI pipeline, including ASR, NLP, and TTS models, on Riva.

Performance Optimization

  • Deploy a production-level conversational AI application with a Helm chart for scaling in Kubernetes clusters.
See More Course Details

Generative AI With Diffusion Models

Skills covered in this course:

Experimentation​

  • Improve the quality of generated images with the denoising diffusion process.​
  • Control the image output with context embeddings. Test and refine the context embeddings to achieve the desired image output, which necessitate experimental approaches to optimize performance.

Multimodal Data

  • Generate images from English text-prompts using contrastive language-image pretraining (CLIP).

Software​ Development

  • Generate images from pure noise.​
  • Generate images from English text prompts using CLIP.

Trustworthy AI​

  • Understand content authenticity and how to build trustworthy models.
See More Details for the Self-Paced Course
See More Details for the Workshop

Building AI Agents with Multimodel Models

Skills covered in this course:

Core Machine Learning and AI Knowledge:​

  • Different data types and how to make them neural networks ready.
  • Model fusion, and the differences between early, late, and intermediate fusion.
  • The difference between modality and agent orchestration.

Multimodal Data:

  • Model fusion, and the differences between early, late, and intermediate fusion.
  • The difference between modality and agent orchestration.

Data Analysis

  • PDF extraction using OCR.

Software Development:

  • Customization of NVIDIA AI Blueprints with VIA.
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