Exam Name: NVIDIA Generative AI Multimodal
Certification Provider: NVIDIA
Corresponding Certification: NVIDIA-Certified Associate
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NVIDIA NCA-GENM Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Multimodal Data | 15% | - Characteristics of text, image, and audio data - Multimodal model architectures and integration - Data preprocessing, fusion, and representation |
| Trustworthy AI | 5% | - Ethical considerations and responsible use - Reliability, fairness, and safety in generative systems - Robustness and error mitigation |
| 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 |
| Experimentation | 25% | - Metrics and validation strategies for generative models - Model training, fine-tuning, and evaluation - Experiment design and methodology |
| Performance Optimization | 10% | - Model efficiency and inference optimization - Hardware acceleration with NVIDIA platforms - Scalability and deployment considerations |
| Core Machine Learning and AI Knowledge | 20% | - Fundamental concepts of machine learning and deep learning - Generative AI principles and techniques - Neural network architectures relevant to multimodal systems |
| Data Analysis and Visualization | 10% | - Analyzing multimodal datasets and outputs - Interpretation of generative AI outputs - Visualization techniques for model behavior and results |
NVIDIA Generative AI Multimodal Sample Questions:
What characteristic of autoencoders makes them suitable for anomaly detection?
- A. Their capacity to learn a compressed representation of the data.
- B. Their ability to classify images with high accuracy.
- C. Their function in enhancing the quality of images.
- D. Their capability to predict future outcomes based on past data.
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Which of the following tasks can be performed using the transformer LLM encoder model?
- A. Image generation
- B. Generating code
- C. Speech recognition
- D. Semantic analysis
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You have a dataset containing information about sales performance for different regions in the last ten years.
Which type of data visualization would be most appropriate to compare the sales performance across regions on a year-by-year basis?
- A. Line chart
- B. Bar chart
- C. Pie chart
- D. Scatter plot
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Which framework is used for conversational AI models development?
- A. NVIDIA Clara
- B. NVIDIA DeepStream
- C. NVIDIA NeMo
- D. NVIDIA Metropolis
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Which metric is commonly used for evaluating Automatic Speech Recognition (ASR) models?
- A. CTC Loss
- B. F1 Score
- C. Word Error Rate (WER)
- D. Mean Opinion Score (MOS)
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