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NVIDIA NCA-GENM Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Trustworthy AI | 5% | - Ethical considerations in AI development - Ensuring fairness and transparency |
| Topic 2: Core ML & AI Knowledge | 20% | - Key algorithms and techniques - Basic concepts and terminology |
| Topic 3: Data Analysis & Visualization | 10% | - Data preprocessing and feature engineering - Visualization techniques for multimodal data |
| Topic 4: Experimentation | 25% | - Hypothesis testing - A/B testing - Experimental design - Model evaluation and comparison |
| Topic 5: Multimodal Data | 15% | - Applications and use cases - Handling and integrating text, image, and audio data |
| Topic 6: Performance Optimization | 10% | - Techniques for optimizing AI performance - Monitoring and improving system efficiency |
| Topic 7: Software Development & Engineering | 15% | - Integration and deployment of multimodal AI systems - Python libraries for multimodal AI |
NVIDIA Generative AI Multimodal Sample Questions:
1. In ML applications, which machine learning algorithm is commonly used for creating new data based on existing data?
A) Decision tree
B) Generative adversarial network (GAN)
C) K-means clustering
D) Support vector machine (SVM)
2. 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
3. What is the significance of A/B testing in ML software engineering?
A) A/B testing is irrelevant in ML software engineering.
B) A/B testing helps in optimizing the hyperparameters of a machine learning model.
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. You are developing a GenAI-Multimodal system that uses data from various sources. What is one potential issue you need to consider in relation to bias in data?
A) Bias in data can only be addressed after the AI system has been deployed.
B) Bias in data is irrelevant as long as the AI system produces accurate predictions.
C) The data used to train the AI system may not be representative of the population it is intended to serve.
D) Bias in data is not a concern for AI systems as they are designed to be neutral and objective.
5. 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
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: C | Question # 3 Answer: C | Question # 4 Answer: C | Question # 5 Answer: A |







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