NVIDIA : NCA-GENM Questions & Answers
Updated: Jul 21, 2026
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NVIDIA NCA-GENM Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Core Machine Learning and AI Knowledge | 20% | - Neural network architectures relevant to multimodal systems - Generative AI principles and techniques - Fundamental concepts of machine learning and deep learning |
| Topic 2: Experimentation | 25% | - Model training, fine-tuning, and evaluation - Experiment design and methodology - Metrics and validation strategies for generative models |
| Topic 3: Software Development and Engineering | 15% | - Development workflows for generative AI applications - Libraries, frameworks, and tools for multimodal AI - Best practices for building and maintaining systems |
| Topic 4: Trustworthy AI | 5% | - Reliability, fairness, and safety in generative systems - Ethical considerations and responsible use - Robustness and error mitigation |
| Topic 5: Performance Optimization | 10% | - Scalability and deployment considerations - Hardware acceleration with NVIDIA platforms - Model efficiency and inference optimization |
| Topic 6: Data Analysis and Visualization | 10% | - Visualization techniques for model behavior and results - Analyzing multimodal datasets and outputs - Interpretation of generative AI outputs |
| Topic 7: Multimodal Data | 15% | - Data preprocessing, fusion, and representation - Multimodal model architectures and integration - Characteristics of text, image, and audio data |
NVIDIA Generative AI Multimodal Sample Questions:
1. How does CLIP understand the content of both text and images?
A) By converting text and images into a frequency domain for comparison.
B) By translating images into text and comparing them with the prompt.
C) Through a database of predefined images with their descriptions.
D) Using contrastive learning to match images with text descriptions.
2. In a Generative Adversarial Network (GAN), what is the role of the discriminator?
A) To calculate the loss function and update the generator.
B) To generate new data based on the training set.
C) To optimize the training process.
D) To distinguish between real and generated data.
3. What role does 'late fusion' play in multimodal machine learning?
A) It refers to the process of combining multiple modalities at the decision level.
B) It refers to the process of combining multiple modalities at the training stage.
C) It refers to the process of combining multiple modalities at the feature level.
D) It refers to the process of combining multiple modalities at the preprocessing stage.
4. What does 'modality alignment' refer to?
A) Addressing challenges related to missing or incomplete information across different modalities.
B) The integration of pretrained models to perform custom tasks involving different types of data.
C) Aligning different modalities within multimodal data to ensure meaningful connections and associations.
D) The process of integrating diverse data types such as text, images, audio, time series, and geospatial information.
5. You are working with a large dataset and want to visualize the distribution of a continuous variable. Which type of data visualization would be most appropriate?
A) Pie chart
B) Histogram chart
C) Bar chart
D) Line chart
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: D | Question # 3 Answer: A | Question # 4 Answer: C | Question # 5 Answer: B |
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