NVIDIA · NCA-GENM

Objective Coverage

Every published NCA-GENM exam objective, matched against the modules that teach it — 39 of 39 objectives have at least one module claiming them today.

1.1Control stability of training in multimodal settings
Covered by M1 · Core Machine Learning and AI Knowledge — 12 of 12 lessons authored.
1.2Develop content for introduction to multimodal loss functions
Covered by M1 · Core Machine Learning and AI Knowledge — 12 of 12 lessons authored.
1.3Familiarity with fundamentals of machine learning (e.g., feature engineering, model comparison, cross validation)
Covered by M1 · Core Machine Learning and AI Knowledge — 12 of 12 lessons authored.
1.4Understand nonsequential neural networks and residual connections
Covered by M1 · Core Machine Learning and AI Knowledge — 12 of 12 lessons authored.
1.5Design statistical analysis for evaluating multimodal pipelines
Covered by M1 · Core Machine Learning and AI Knowledge — 12 of 12 lessons authored.
1.6Develop content for multimodal-specific transfer learning
Covered by M1 · Core Machine Learning and AI Knowledge — 12 of 12 lessons authored.
1.7Familiarity with emerging multimodal trends and technologies
Covered by M1 · Core Machine Learning and AI Knowledge — 12 of 12 lessons authored.
1.8Contribute to the design, development, and deployment of energy-efficient and trustworthy multimodal AI models
Covered by M1 · Core Machine Learning and AI Knowledge — 12 of 12 lessons authored.
1.9Use prompt engineering principles to create prompts to achieve desired results
Covered by M1 · Core Machine Learning and AI Knowledge — 12 of 12 lessons authored.
1.10Understand deep learning frameworks such as TensorFlow or PyTorch
Covered by M1 · Core Machine Learning and AI Knowledge — 12 of 12 lessons authored.
2.1Awareness of the process of extracting insights from large datasets using data mining, data visualization, and similar techniques
Covered by M2 · Data Analysis — 6 of 6 lessons authored.
2.2Develop content for attention maps in multimodal settings
Covered by M2 · Data Analysis — 6 of 6 lessons authored.
2.3Create graphs, charts, or other visualizations to convey the results of data analysis using specialized software
Covered by M2 · Data Analysis — 6 of 6 lessons authored.
2.4Identify relationships and trends or any factors that could affect the results of research
Covered by M2 · Data Analysis — 6 of 6 lessons authored.
3.1Assist in developing and testing multimodal AI models
Covered by M3 · Experimentation — 9 of 9 lessons authored.
3.2Manage and preprocess data from various sources
Covered by M3 · Experimentation — 9 of 9 lessons authored.
3.3Use multimodal models to improve explainability
Covered by M3 · Experimentation — 9 of 9 lessons authored.
3.4Test data quality and consistency in a multimodal setting
Covered by M3 · Experimentation — 9 of 9 lessons authored.
3.5Test AI models to ensure their accuracy and effectiveness
Covered by M3 · Experimentation — 9 of 9 lessons authored.
4.1Assist in the deployment and evaluations of model scalability, performance, and reliability under the supervision of senior team member
Covered by M4 · Multimodal Data — 7 of 7 lessons authored.
4.2Build LLM use cases such as retrieval-augmented generation (RAG), chatbots, and summarizers
Covered by M4 · Multimodal Data — 7 of 7 lessons authored.
4.3Familiarity with the capabilities of Python natural language packages (spaCy, NumPy, vector databases, etc.)
Covered by M4 · Multimodal Data — 7 of 7 lessons authored.
4.4Identify system data, hardware, or software components required to meet user needs
Covered by M4 · Multimodal Data — 7 of 7 lessons authored.
4.5Monitor the functioning of data collection, experiments, and other software processes
Covered by M4 · Multimodal Data — 7 of 7 lessons authored.
4.6Use Python packages (spaCy, NumPy, Keras, etc.) to implement specific traditional machine learning analyses
Covered by M4 · Multimodal Data — 7 of 7 lessons authored.
4.7Write software components or scripts under the supervision of a senior team member
Covered by M4 · Multimodal Data — 7 of 7 lessons authored.
5.1Enhance computational efficiency and improve the accuracy of outputs in AI models
Covered by M5 · Performance Optimization — 6 of 6 lessons authored.
5.2Optimize the performance of AI models, including tuning hyperparameters
Covered by M5 · Performance Optimization — 6 of 6 lessons authored.
5.3Develop content for multimodal-specific transfer learning
Covered by M5 · Performance Optimization — 6 of 6 lessons authored.
5.4Assist in model training and training optimization under the supervision of a senior team member
Covered by M5 · Performance Optimization — 6 of 6 lessons authored.
6.1Collaborate with the client during requirements acquisition, data gathering, progress reporting, deployment, and integration
Covered by M6 · Software Development — 6 of 6 lessons authored.
6.2Ensure adherence to best practices and maintain high standards of software quality and reliability
Covered by M6 · Software Development — 6 of 6 lessons authored.
6.3Use prompt engineering to better influence the output of generative AI models
Covered by M6 · Software Development — 6 of 6 lessons authored.
6.4Build a U-Net to generate images from pure noise and as a type of autoencoder
Covered by M6 · Software Development — 6 of 6 lessons authored.
6.5Generate images from English text prompts using CLIP, and use CLIP to train a text-to-image diffusion model
Covered by M6 · Software Development — 6 of 6 lessons authored.
7.1Describe the ethical principles of trustworthy AI
Covered by M7 · Trustworthy AI — 5 of 5 lessons authored.
7.2Describe the balance between data privacy and the importance of data consent
Covered by M7 · Trustworthy AI — 5 of 5 lessons authored.
7.3Describe how to use NVIDIA and other technologies to improve AI trustworthiness
Covered by M7 · Trustworthy AI — 5 of 5 lessons authored.
7.4Describe how to minimize bias in AI systems
Covered by M7 · Trustworthy AI — 5 of 5 lessons authored.

Back to the NCA-GENM prep course.