NVIDIA-Certified Associate

Generative AI Multimodal

(NCA-GENM)

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: $135

Certification level: Associate

Subject: Multimodal generative AI

Number of questions: 50 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

Generative AI Explained
Self-paced | 2 hours | Free

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

You can take one of these courses:
Accelerating End-to-End Data Science Workflows
Self-paced | 6 hours | $90

Fundamentals of Accelerated Data Science
Workshop | 8 hours | $500

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

Computer Vision for ​Industrial Inspection
Workshop | 8 hours | $500

Applications of AI for ​ Anomaly Detection
Workshop | 8 hours | $500

Applications of AI for ​ Predictive Maintenance
Workshop | 8 hours | $500

Introduction to Transformer-Based ​ Natural Language Processing
Self-paced | 6 hours | $30

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

Rapid Application Development With ​Large Language Models (LLMs)
Workshop | 8 hours | $500

Efficient Large Language Model (LLM) Customization
Workshop | 8 hours | $500

Deploying a Model for Inference ​At Production Scale
Self-paced | 4 hours | $30

Review These Additional Materials

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Generative AI Explained

Skills covered in this course:

Core Machine Learning and AI Knowledge

  • Define generative AI and explain how it works. ​
  • Describe various generative AI applications. ​
  • Explain the challenges and opportunities in generative AI.

You can take one of these courses:

Getting Started With Deep Learning
Fundamentals of Deep Learning

Skills covered in these courses:

Experimentation

  • 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.

Software Development

  • Gain experience with common deep learning data types and model architectures. 
  • Leverage transfer learning between models to achieve efficient results with less data and computation. 
  • Take on your own project with a modern deep learning framework.

You can take one of these courses:

​Accelerating End-to-End Data Science Workflows
Fundamentals of Accelerated Data Science

Skills covered in these courses:

Software​ Development

  • Implement GPU-accelerated data preparation and feature extraction using cuDF and Apache Arrow data frames​.
  • Apply a broad spectrum of GPU-accelerated machine learning tasks using XGBoost and a variety of cuML algorithms.​
  • Execute GPU-accelerated graph analysis with cuGraph, achieving massive-scale analytics in small amounts of time.​
  • Rapidly achieve massive-scale graph analytics using cuGraph routines.

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.

Computer Vision for ​Industrial Inspection

Skills covered in this course:

Performance​ Optimization​

  • Extract meaningful insights from the provided dataset using pandas DataFrame.​
  • Apply transfer learning to a deep learning classification model.​
  • Fine-tune the deep learning model and set up evaluation metrics.​
  • Deploy and measure model performance.​
  • Experiment with various inference configurations to optimize model performance.

Applications of AI for ​Anomaly Detection

Skills covered in this course:

Multimodal Data

  • Prepare data and build, train, and evaluate models using XGBoost, autoencoders, and generative adversarial networks (GANs).​
  • Detect anomalies in datasets with both labeled and unlabeled data​.
  • Classify anomalies into multiple categories regardless of whether the original data was labeled.

Applications of AI for ​Predictive Maintenance

Skills covered in this course:

Multimodal Data

  • Use time-series data to predict outcomes with XGBoost-based machine learning classification models.​
  • Use anomaly detection with time-series autoencoders to predict failures when limited failure-example data is available.

Introduction to Transformer-Based Natural Language Processing

Skills covered in this course:

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

Software Development

  • Leverage pretrained, modern LLMs to solve various NLP tasks such as token classification, text classification, summarization, and question-answering.

Data Analysis​ and Visualization​

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

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.

Rapid Application Development With ​Large Language Models (LLMs)

Skills covered in this course:

Experimentation

  • Find, pull in, and experiment with the Hugging Face model repository and the associated transformers API.​
  • Use encoder models for tasks like semantic analysis, embedding, question-answering, and zero-shot classification.​
  • Use decoder models to generate sequences like code, unbounded answers, and conversations.

Software Development

  • Find, pull in, and experiment with the Hugging Face model repository and the associated transformers API​.​
  • Use state management and composition techniques to guide LLMs for safe, effective, and accurate conversation.

Trustworthy AI​

  • Use state management and composition techniques to guide LLMs for safe, effective, and accurate conversation.

Efficient Large Language Model (LLM) Customization

Skills covered in this course:

Core Machine Learning and AI Knowledge​

  • Know how to apply fine-tuning techniques.
  • Understand how to effectively integrate and interpret diverse data types within a single model framework.

Multimodal Data

  • Use a single pretrained model to perform multiple custom tasks involving different types of data (e.g., text, images, audio).

Software Development

  • Leverage the NVIDIA NeMo™ framework to customize models like GPT, LLaMA-2, and Falcon with ease.

Data Analysis​ and Visualization​

  • Assess the performance of fine-tuned models.

Performance​ Optimization​

  • Use fine-tuning to perform optimization to enhance a model's accuracy, efficiency, or effectiveness for specific tasks.

Deploying a Model for Inference ​at Production Scale

Skills covered in this course:

Software Development

  • Deploy neural networks from a variety of frameworks onto a live NVIDIA Triton™ server.​
  • Measure GPU usage and other metrics with Prometheus.​
  • Send asynchronous requests to maximize throughput.

Performance​ Optimization​

  • Measure GPU usage and other metrics with Prometheus​.
  • Send asynchronous requests to maximize throughput.