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NVIDIA NCA-GENM : NVIDIA Generative AI Multimodal
Exam Code: NCA-GENM
Exam Name: NVIDIA Generative AI Multimodal
Updated: Sep 10, 2026
Q & A: 58 Questions and Answers
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NVIDIA NCA-GENM Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Trustworthy AI | 5% | - Ensuring fairness and transparency - Ethical considerations in AI development |
| Topic 2: Core ML & AI Knowledge | 20% | - Key algorithms and techniques - Basic concepts and terminology |
| Topic 3: Multimodal Data | 15% | - Handling and integrating text, image, and audio data - Applications and use cases |
| Topic 4: Data Analysis & Visualization | 10% | - Data preprocessing and feature engineering - Visualization techniques for multimodal data |
| Topic 5: Software Development & Engineering | 15% | - Integration and deployment of multimodal AI systems - Python libraries for multimodal AI |
| Topic 6: Experimentation | 25% | - Hypothesis testing - A/B testing - Model evaluation and comparison - Experimental design |
| Topic 7: Performance Optimization | 10% | - Techniques for optimizing AI performance - Monitoring and improving system efficiency |
NVIDIA Generative AI Multimodal Sample Questions:
Question #1
What is the role of CLIP (Contrastive Language-Image Pretraining) in text-to-image generation?
- A. CLIP is used to generate image captions from textual input.
- B. CLIP provides a common embedding space for both the textual and image modalities.
- C. CLIP is used to convert textual input into image embeddings.
- D. CLIP is used to enhance datasets through data augmentation for text-to-image generation.
Answer: B
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Question #2
Which technique is commonly used to speed up AI model training and inference on hardware accelerators?
- A. Dropout
- B. Quantization
- C. Model enlargement
- D. Data augmentation
Answer: B
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Question #3
How does CLIP understand the content of both text and images?
- A. Using contrastive learning to match images with text descriptions.
- B. Through a database of predefined images with their descriptions.
- C. By translating images into text and comparing them with the prompt.
- D. By converting text and images into a frequency domain for comparison.
Answer: A
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Question #4
How does the batch size influence VRAM consumption during inference with ML models on GPUs?
- A. Increasing or decreasing the batch size has the same impact on VRAM consumption.
- B. The batch size has no impact on VRAM consumption during inference.
- C. Decreasing the batch size reduces VRAM consumption.
- D. Increasing the batch size reduces VRAM consumption because more data can be processed in parallel.
Answer: C
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Question #5
What is the significance of A/B testing in ML software engineering?
- A. A/B testing helps in evaluating the performance and effectiveness of different machine learning models.
- B. A/B testing is used to measure the impact of changes in the user interface of a ML application.
- C. A/B testing is irrelevant in ML software engineering.
- D. A/B testing helps in optimizing the hyperparameters of a machine learning model.
Answer: A
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