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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Experimentation | 25% | - A/B testing - Experimental design - Hypothesis testing - Model evaluation and comparison |
| Topic 2: Multimodal Data | 15% | - Handling and integrating text, image, and audio data - Applications and use cases |
| Topic 3: Software Development & Engineering | 15% | - Integration and deployment of multimodal AI systems - Python libraries for multimodal AI |
| Topic 4: Data Analysis & Visualization | 10% | - Visualization techniques for multimodal data - Data preprocessing and feature engineering |
| Topic 5: Performance Optimization | 10% | - Monitoring and improving system efficiency - Techniques for optimizing AI performance |
| Topic 6: Core ML & AI Knowledge | 20% | - Basic concepts and terminology - Key algorithms and techniques |
| Topic 7: Trustworthy AI | 5% | - Ensuring fairness and transparency - Ethical considerations in AI development |
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NEW QUESTION # 13
You're building a system that takes a medical image (e.g., X-ray) and a patient's medical history (text) as input, predicting the likelihood of a specific disease. You want to use SHAP (SHapley Additive exPlanations) values to explain the model's predictions. How would you adapt SHAP to handle both image and text inputs effectively?
Answer: A
Explanation:
The best approach is to use a multimodal SHAP implementation that considers the interaction between image and text features. This ensures a holistic explanation of the model's prediction based on both modalities. Treating them separately or simply concatenating features ignores potential synergistic effects.
NEW QUESTION # 14
Which of the following NVIDIA SDKs is most suitable for deploying a real-time, low-latency speech-to-text service as part of a multimodal AI application?
Answer: E
Explanation:
NVIDIA Riva is specifically designed for building and deploying real-time conversational A1 services, including speech-to-text, text-to- speech, and natural language understanding. Its focus is on low latency and high accuracy for conversational applications.
NEW QUESTION # 15
You are building a multimodal model that combines text and images to generate product descriptions. The text data is tokenized using spaCy, and the image data is represented as feature vectors extracted from a pre-trained ResNet model. How can you effectively align and fuse these heterogeneous data types before feeding them into a downstream generative model?
Answer: A,E
Explanation:
Direct concatenation or averaging doesn't capture the complex relationships between modalities. Cross-modal attention allows the model to learn which parts of the image are most relevant to the text, leading to better alignment and fusion. Projecting both modalities into a common embedding space allows for a unified representation that can be effectively used by the downstream generative model.
NEW QUESTION # 16
Consider the following Python code snippet using PyTorch Lightning and a Hugging Face Transformers model for multimodal classification. Which of the following code snippets is MOST appropriate to perform gradient accumulation in this context, assuming you want to accumulate gradients over 4 batches?
Answer: D
Explanation:
PyTorch Lightning provides a built-in argument in the 'Trainer' class to easily enable gradient accumulation. Setting will accumulate gradients over 4 batches before performing an optimizer step.
NEW QUESTION # 17
You're working with a Text-to-Image generation model and want to improve its explainability by visualizing which words in the text prompt are most influential in generating specific regions of the output image. What technique would be most suitable for achieving this?
Answer: A
Explanation:
Attention maps from the cross-attention layers directly indicate which words in the text prompt the image decoder attends to when generating different regions of the image. This provides a visual explanation of the relationship between text and image generation. Grad-CAM is more suited for explaining image classification models. SHAP values can explain the importance of words in the text, but not their spatial influence in the generated image.
NEW QUESTION # 18
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