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

    Review study guide

    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

     

    Contact Us

    NVIDIA offers training and certification for professionals looking to enhance their skills and knowledge in the field of AI, accelerated computing, data science, advanced networking, graphics, simulation, and more.

    Contact us to learn how we can help you achieve your goals.

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    Get training news, announcements, and more from NVIDIA, including the latest information on new self-paced courses, instructor-led workshops, free training, discounts, and more. You can unsubscribe at any time.

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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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