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| Section | Weight | Objectives |
|---|---|---|
| Multimodal Data | 15% | - Data preprocessing, fusion, and representation - Characteristics of text, image, and audio data - Multimodal model architectures and integration |
| Data Analysis and Visualization | 10% | - Visualization techniques for model behavior and results - Interpretation of generative AI outputs - Analyzing multimodal datasets and outputs |
| Trustworthy AI | 5% | - Reliability, fairness, and safety in generative systems - Robustness and error mitigation - Ethical considerations and responsible use |
| 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 |
| 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 |
| Performance Optimization | 10% | - Hardware acceleration with NVIDIA platforms - Model efficiency and inference optimization - Scalability and deployment considerations |
| Experimentation | 25% | - Model training, fine-tuning, and evaluation - Metrics and validation strategies for generative models - Experiment design and methodology |
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NEW QUESTION # 18
When training a Variational Autoencoder (VAE) for generating new data points, which of the following objectives does the VAE optimize?
Answer: E
Explanation:
A VAE optimizes all three objectives. It aims to maximize the likelihood of the input data given the latent representation (reconstruction accuracy), minimize the KL divergence to ensure the latent space is well-structured and smooth, and maximize the similarity between the input and reconstructed data to ensure effective encoding and decoding.
NEW QUESTION # 19
Which of the following is NOT a typical application or benefit of using U-Net architectures in generative AI, particularly within the context of image generation and manipulation?
Answer: C
Explanation:
U-Nets are primarily used for image-to-image tasks like segmentation, inpainting, and super-resolution. They excel at processing and generating images, but are not directly involved in encoding text data. CLIP is used for that purpose.
NEW QUESTION # 20
You are developing a multimodal system for generating recipes from images of food. The system takes an image of a dish as input and outputs a recipe containing the ingredients and instructions. Which of the following evaluation metrics would be most suitable for assessing the correctness and completeness of the generated recipes? (Select all that apply)
Answer: A,C
Explanation:
Precision and recall of ingredients directly assess whether the generated recipe includes the correct ingredients. Human evaluation provides a subjective assessment of the recipe's overall quality, clarity, and accuracy. BLEU score is a general text evaluation metric, but may not capture the specific requirements of recipe generation. Inception Score is relevant for image generation, not recipe generation. Cosine similarity of word embeddings can capture semantic similarity, but doesn't guarantee correctness of the recipe.
NEW QUESTION # 21
When building a multimodal chatbot that handles both text and voice inputs, what are the primary challenges related to data alignment and synchronization that you need to address?
Answer: E
Explanation:
All the options mentioned are challenges related to data alignment and synchronization. The vocabulary of encoders, handling paraphrasing between text and voice, and dealing with variations in speech are critical aspects of building a robust multimodal chatbot.
NEW QUESTION # 22
You are building a multimodal model that takes images and text descriptions as input to generate new images. You want to evaluate the impact of different image encoders (ResNet50, Efficient Net) on the generated image quality and relevance to the text prompt. Which evaluation metric(s) would be MOST appropriate for this task?
Answer: C
Explanation:
FID measures the distance between the feature distributions of generated and real images, indicating image quality and diversity. CLIP Score measures the similarity between the generated image and the text prompt, evaluating relevance. IS more suitable for evaluating unimodal image generation. Perplexity and BLEU score are for text generation.
NEW QUESTION # 23
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