NVIDIA Generative AI Multimodal : NCA-GENM Exam Questions

Exam Code: NCA-GENM
Exam Name: NVIDIA Generative AI Multimodal
Updated: Aug 08, 2026
Q&As: 58 Questions and Answers

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NVIDIA NCA-GENM Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Analysis and Visualization	10%	- Analyzing multimodal datasets and outputs - Interpretation of generative AI outputs - Visualization techniques for model behavior and results
Topic 2: 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 3: Experimentation	25%	- Model training, fine-tuning, and evaluation - Experiment design and methodology - Metrics and validation strategies for generative models
Topic 4: Multimodal Data	15%	- Characteristics of text, image, and audio data - Multimodal model architectures and integration - Data preprocessing, fusion, and representation
Topic 5: Core Machine Learning and AI Knowledge	20%	- Fundamental concepts of machine learning and deep learning - Neural network architectures relevant to multimodal systems - Generative AI principles and techniques
Topic 6: Performance Optimization	10%	- Scalability and deployment considerations - Hardware acceleration with NVIDIA platforms - Model efficiency and inference optimization
Topic 7: Trustworthy AI	5%	- Robustness and error mitigation - Reliability, fairness, and safety in generative systems - Ethical considerations and responsible use

NVIDIA Generative AI Multimodal Sample Questions:

1. Which of the following is a disadvantage of the ReLU activation function?

A) It is computationally expensive.
B) It is not suitable for deep neural networks.
C) It is prone to vanishing gradient problem.
D) It can cause dead neurons.

2. You are evaluating the performance of an AI model for facial recognition. What is an important consideration when evaluating the model for bias?

A) The model's accuracy in recognizing individuals of different races.
B) The model's processing speed in recognizing faces of different races.
C) The model's compatibility with different operating systems.
D) The model's ability to recognize various facial expressions.

3. Which of the following tasks can be performed using the transformer LLM encoder model?

A) Semantic analysis
B) Image generation
C) Speech recognition
D) Generating code

4. You are tasked with developing an image processing model using machine learning. You need to classify thousands of labeled images of cats and dogs. Which algorithm is commonly used for image classification?

A) Linear Regression
B) K-Means Clustering
C) Convolutional Neural Networks (CNN)
D) Decision Trees

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) Bar chart
B) Pie chart
C) Line chart
D) Histogram chart

Solutions:

Question # 1
Answer: D

Question # 2
Answer: A

Question # 3
Answer: A

Question # 4
Answer: C

Question # 5
Answer: D