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NVIDIA-Certified Associate: Generative AI Multimodal (NCA-GENM) Syllabus

Core Machine Learning and AI Knowledge

Applying ML and deep learning fundamentals to multimodal models.

20%

Weight

Questions

Marks

ML and deep learning fundamentals

Feature engineering, model comparison and cross-validation
Non-sequential networks and residual connections
Deep learning frameworks such as PyTorch and TensorFlow
Prompt engineering principles

Multimodal training concepts

Stabilizing training in multimodal settings
Multimodal loss functions
Transfer learning for multimodal models
Energy-efficient and trustworthy multimodal models
Emerging multimodal trends

Data Analysis

Extracting insights and visualizing results, including attention maps.

10%

Weight

Questions

Marks

Data mining and visualization

Data mining large datasets
Charts and graphs with specialized software
Identifying relationships and trends
Factors that affect results

Multimodal analysis

Attention maps in multimodal settings
Statistical analyses for multimodal pipelines
Interpreting evaluation metrics
Comparing model results

Experimentation

Developing, testing and evaluating multimodal models and data pipelines.

25%

Weight

Questions

Marks

Model development and testing

Developing and testing multimodal models
Testing models for accuracy and effectiveness
Using multimodal models to improve explainability
Comparing experiment results

Data handling in experiments

Managing and preprocessing data from multiple sources
Testing data quality across modalities
Consistency checks between modalities
Dataset splits for evaluation

Multimodal Data

Handling the data, tools and requirements behind multimodal and LLM applications.

15%

Weight

Questions

Marks

Data tooling and applications

Python data tools such as spaCy, NumPy and vector databases
RAG, chatbot and summarizer use cases
Traditional ML analysis with Keras
Writing data scripts and components

Requirements and monitoring

Identifying data, hardware and software requirements
Monitoring data collection and experiments
Evaluating scalability, performance and reliability
Pairing and aligning image, audio and text data

Performance Optimization

Improving the efficiency and accuracy of multimodal models.

10%

Weight

Questions

Marks

Efficiency and tuning

Improving computational efficiency
Improving output accuracy
Hyperparameter tuning
Training optimization

Transfer learning

Multimodal transfer learning
Reusing pretrained encoders
Fine-tuning pretrained models
Evaluating optimization trade-offs

Software Development

Building generative image and multimodal applications to client requirements.

15%

Weight

Questions

Marks

Generative model building

U-Net architectures that generate images from noise
Autoencoders
Text-to-image generation with CLIP
Training text-to-image diffusion models with CLIP guidance

Engineering practice

Gathering requirements with clients
Deployment and integration
Software quality and reliability best practices
Prompt engineering to steer generative outputs

Trustworthy AI

Applying ethics, privacy and bias mitigation to multimodal AI.

Weight

Questions

Marks

Ethics and privacy

Ethical principles of trustworthy AI
Balancing data privacy and consent
Consent for image and audio data
Transparency in AI systems

Bias and trust tooling

Minimizing bias in AI systems
NVIDIA technologies for trustworthiness
Energy-efficient, trustworthy multimodal models
Evaluating fairness of outputs

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Timed Exams

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Mind Maps

Visual learning tools to connect and remember concepts

Interactive diagrams
Topic relationships

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Learning Progress

Core Machine Learning and AI Knowledge 85%

Data Analysis 92%

Practice Test Scores

95%

Latest Score

Above passing threshold

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