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NVIDIA NCA-GENM Exam Syllabus

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Before starting your NCA-GENM exam preparation, it is recommended to review the complete NVIDIA Generative AI Multimodal exam syllabus and carefully go through the exam objectives listed below. Once you understand the exam structure and objectives, you should practice using our free NCA-GENM questions. We also provide premium NCA-GENM practice test, fully updated according to the latest exam objectives, to help you accurately assess your preparedness for the actual exam.

NVIDIA
Vendor
NCA-GENM
Exam Code
56
Total Questions
7
Total Exam Domains

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NCA-GENM EXAM QUESTIONS

NVIDIA NCA-GENM Exam Objectives

Section 1: Core Machine Learning and AI Knowledge
Weight:
20%
  • 1.1 Control stability of training in multimodal settings
  • 1.2 Develop content for introduction to multimodal loss functions.
  • 1.3 Familiarity with fundamentals of machine learning (e.g., feature engineering, model comparison, cross validation).
  • 1.4 Understand nonsequential neural networks and residual connections.
  • 1.5 Design statistical analysis for evaluating multimodal pipelines.
  • 1.6 Develop content for multimodal-specific transfer learning.
  • 1.7 Familiarity with emerging multimodal trends and technologies.
  • 1.8 Contribute to the design, development, and deployment of energy-efficient and trustworthy multimodal AI models.
  • 1.9 Use prompt engineering principles to create prompts to achieve desired results.
  • 1.10 Understand deep learning frameworks such as TensorFlow or PyTorch
Section 2: Data Analysis
Weight:
10%
  • 2.1 Awareness of the process of extracting insights from large datasets using data mining, data visualization, and similar techniques.
  • 2.2 Develop content for attention maps in multimodal settings.
  • 2.3 Create graphs, charts, or other visualizations to convey the results of data analysis using specialized software.
  • 2.4 Identify relationships and trends or any factors that could affect the results of research
Section 3: Experimentation
Weight:
25%
  • 3.1 Assist in developing and testing multimodal AI models.
  • 3.2 Manage and preprocess data from various sources.
  • 3.3 Use multimodal models to improve explainability.
  • 3.4 Test data quality and consistency in a multimodal setting.
  • 3.5 Test AI models to ensure their accuracy and effectiveness
Section 4: Multimodal Data
Weight:
15%
  • 4.1 Assist in the deployment and evaluations of model scalability, performance, and reliability under the supervision of senior team member.
  • 4.2 Build LLM use cases such as retrieval-augmented generation (RAG), chatbots, and summarizers.
  • 4.3 Familiarity with the capabilities of Python natural language packages (spaCy, NumPy, vector databases, etc.).
  • 4.4 Identify system data, hardware, or software components required to meet user needs.
  • 4.5 Monitor the functioning of data collection, experiments, and other software processes.
  • 4.6 Use Python packages (spaCy, NumPy, Keras, etc.) to implement specific traditional machine learning analyses.
  • 4.7 Write software components or scripts under the supervision of a senior team member
Section 5: Performance Optimization
Weight:
10%
  • 5.1 Enhance computational efficiency and improve the accuracy of outputs in AI models.
  • 5.2 Optimize the performance of AI models, including tuning hyperparameters.
  • 5.3 Develop content for multimodal-specific transfer learning.
  • 5.4 Assist in model training and training optimization under the supervision of a senior team member.
Section 6: Software Development
Weight:
15%
  • 6.1 Collaborate with the client during requirements acquisition, data gathering, progress reporting, deployment, and integration.
  • 6.2 Ensure adherence to best practices and maintain high standards of software quality and reliability.
  • 6.3 Use prompt engineering to better influence the output of generative AI models.
  • 6.4 Build a U-Net to generate images from pure noise and as a type of autoencoder.
  • 6.5 Generate images from English text prompts using CLIP, and use CLIP to train a text-to-image diffusion model.
Section 7: Trustworthy AI
Weight:
5%
  • 7.1 Describe the ethical principles of trustworthy AI.
  • 7.2 Describe the balance between data privacy and the importance of data consent.
  • 7.3 Describe how to use NVIDIA and other technologies to improve AI trustworthiness.
  • 7.4 Describe how to minimize bias in AI systems.
Info
https://www.nvidia.com/en-us/learn/certification/generative-ai-multimodal-associate/