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Preparing for Nvidia Multimodal Generative AI NCA GENM Certification

The NCA Generative AI Multimodal certification (NCA-GENM) from NVIDIA will test your knowledge of multi-modal(text, images, videos and voice) AI systems. The student is expected to have a foundational understanding to design, build, architect and operationalize multi-modal AI systems. It is a entry level certification, it does not go deep on topics. You also need to know the relevant NVIDIA products Since I had already received the GenAI cert, the text modality was done for me. I needed to focus on Image, Video and Audio modality. I first focused on Image modality and then on Audio as below.

Deep Learning and Neural Networks

  • Understand the motivation for building neural networks, and the use cases they address.
  • Understand the key components of a deep neural network architecture: like Nodes, Hidden layers, Activation functions, Loss Functions.
  • High-level understanding of how neural networks are trained using backpropagation and loss functions.
  • Understand how they are deployed for inference and how NVIDIA products are used.
  • Understand use cases of neural networks vs machine learning algorithms.
  • CNN and GAN (Generative Adversarial Network).
References

Transformer Architecture and NLP

This is very important. You need to have a complete high level understanding of the Transformer architecture Needless to stay, since this certification is about GenAI you need to have a intuition level understanding of NLP. There will be mentions of Word2Vec, RNNs, LSTM, etc and it can get confusing and overwhelming pretty quickly. The way I wrapped my head around this is to understand the history of NLP and how it lead to transformer based NLP.
  • RNNs
  • Word2Vec
  • LLM Benchmarks
  • Layer Normalization
References

I learnt the different types of data used in multi-modal applications.

Data Type Source/Sensor Data Format What It Represents Use Cases
Text Reports, Commands Plain text, JSON, XML Human language — descriptions, labels, commands Medical notes, robot instructions, annotations
Image Cameras (RGB, grayscale) 2D array (H×W×Channels), PNG, JPEG Visual scene in 2D Object detection, diagnosis, navigation
CT Scan Medical CT Scanner 3D volume (H×W×Slices), DICOM, NIfTI Internal body structures in 3D Tumor detection, organ segmentation
LiDAR LiDAR Sensor Point cloud (x, y, z), PCD, LAS files 3D structure and depth of environment Robot/vehicle navigation, obstacle detection
Radar Radar Sensor Signal data → Range-Doppler maps Object distance, speed, motion detection Automotive safety, weather tracking



AI and Computer Vision

Then I spent 2-3 weeks on computer vision. What really worked for me and avoid confusion in the different machine learning architecture was to first create a timeline of the history of AI and computer vision. Then I attacked each topic, it’s architecture, how it works, use cases and limitations. I recommend you have your fundamentals clear on each type of architecture, how it works and business use cases . The questions will be scenario based.
AI and Computer Vision Timeline
1950s–1980s: Early AI & Computer Vision
  • 1950s: AI concept introduced; early work on pattern recognition.
  • 1960s: First attempts at computer vision—simple edge detection.
  • 1980s: Neural networks (e.g., perceptron) explored but limited by hardware.
1990s–2000s: Machine Learning Era
  • 1990s: Shift from rule-based vision to statistical machine learning.
  • 1998: LeNet-5 (by Yann LeCun) – Early CNN for handwritten digit recognition.
2010s: Deep Learning Breakthroughs
  • 2012: AlexNet (CNN) revolutionizes image recognition, winning ImageNet competition.
  • 2013–2014: VAE (Variational Autoencoders) introduced – Early deep generative model for creating images by learning latent representations.
  • 2014: GANs (Generative Adversarial Networks) introduced – first major leap in AI-generated images.
  • 2015: U-Net introduced – A CNN designed for image segmentation, later key in diffusion models.
  • 2015: DeepDream & Neural Style Transfer – AI creates surreal and artistic images.
2020s: AI-Generated Content Boom
  • 2020: U-Net becomes core to diffusion models, enabling AI image generation.
  • 2021: CLIP (by OpenAI) introduced – Enables vision-language understanding using contrastive learning.
Generative AI & Diffusion Model References

Voice

I spent time on below topics to be absolutely clear on building and deploying voice pipelines that combine voice and text.

  • Building a complete conversational AI pipeline that includes automatic speech recognition (ASR), natural language processing (NLP), and text-to-speech (TTS).
  • Using the NVIDIA NeMo framework to customize ASR and TTS models for real-world scenarios.
  • What is NVIDIA Riva and how do you use it to create voice, multilingual speech, transcription, and translation AI apps.
  • Fundamentals on how to build and deploy a full conversational AI system using ASR, NLP, and TTS.
  • How to measure the quality of voice apps.
Speech AI References

Multi-Modal AI apps

To prepare for the certification, you need to understand how multimodal fusion works in real-world AI systems. Different types of fusion and when to use what:

  • Early and Late Fusion
  • Intermediate Fusion

Other important concepts

  • CLIP architecture
  • Contrastive Pretraining model
  • How to evaluate ASR models using Word Error Rate (WER) and Real-Time Factor (RTF)
References