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Instant Download NVIDIA : NCA-GENM Questions & Answers as PDF & Test Engine
- Exam Code: NCA-GENM
- Exam Name: NVIDIA Generative AI Multimodal
- Updated: Jul 24, 2026
- No. of Questions: 58 Questions and Answers
- Download Limit: Unlimited
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NVIDIA NCA-GENM Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Performance Optimization | 10% | - Monitoring and improving system efficiency - Techniques for optimizing AI performance |
| Topic 2: Multimodal Data | 15% | - Handling and integrating text, image, and audio data - Applications and use cases |
| Topic 3: Experimentation | 25% | - A/B testing - Hypothesis testing - Model evaluation and comparison - Experimental design |
| Topic 4: Data Analysis & Visualization | 10% | - Data preprocessing and feature engineering - Visualization techniques for multimodal data |
| Topic 5: Software Development & Engineering | 15% | - Python libraries for multimodal AI - Integration and deployment of multimodal AI systems |
| Topic 6: Trustworthy AI | 5% | - Ensuring fairness and transparency - Ethical considerations in AI development |
| Topic 7: Core ML & AI Knowledge | 20% | - Basic concepts and terminology - Key algorithms and techniques |
NVIDIA Generative AI Multimodal Sample Questions:
1. In experimentation, how does data augmentation contribute to improving model accuracy?
A) It reduces the complexity of the model, making it easier to train and evaluate.
B) It helps in increasing the size of the dataset, leading to better generalization of the model.
C) It has no impact on model accuracy and is primarily used for data visualization purposes.
D) It improves the interpretability of the model by providing additional insights into the data.
2. You want to evaluate the performance of an AI model. Which of the following is a method for AI model evaluation?
A) Calculating the model's accuracy from randomly selected data points from the dataset not used during the model's training.
B) Calculating the loss function of the model on the training set.
C) Randomly selecting data points from the training set and calculating the accuracy of the model on these data points.
D) Interviewing the developers of the AI model to assess its performance.
3. What is the significance of A/B testing in ML software engineering?
A) A/B testing helps in optimizing the hyperparameters of a machine learning model.
B) A/B testing is irrelevant in ML software engineering.
C) A/B testing helps in evaluating the performance and effectiveness of different machine learning models.
D) A/B testing is used to measure the impact of changes in the user interface of a ML application.
4. Which of the following is a component of the Content Authenticity Initiative?
A) Content credential
B) Content validity
C) Ethical AI development
D) Data encryption
5. What is a common method to reduce the computational cost of deep learning models during inference?
A) Adding more convolutional filters.
B) Increasing the batch size.
C) By replacing activation functions in some neurons with simpler ones.
D) Pruning weights or neurons.
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: A | Question # 3 Answer: C | Question # 4 Answer: A | Question # 5 Answer: D |
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