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| Section | Weight | Objectives |
|---|---|---|
| Multimodal Data | 15% | - Characteristics of text, image, and audio data - Data preprocessing, fusion, and representation - Multimodal model architectures and integration |
| 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% | - Interpretation of generative AI outputs - Analyzing multimodal datasets and outputs - Visualization techniques for model behavior and results |
| Trustworthy AI | 5% | - Robustness and error mitigation - Reliability, fairness, and safety in generative systems - Ethical considerations and responsible use |
| Experimentation | 25% | - Metrics and validation strategies for generative models - Model training, fine-tuning, and evaluation - Experiment design and methodology |
| Performance Optimization | 10% | - Scalability and deployment considerations - Model efficiency and inference optimization - Hardware acceleration with NVIDIA platforms |
| Software Development and Engineering | 15% | - Best practices for building and maintaining systems - Development workflows for generative AI applications - Libraries, frameworks, and tools for multimodal AI |
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NEW QUESTION # 37
You are building a video summarization system that uses both visual (frame content) and audio (speech transcripts) information. You've noticed that the system tends to prioritize segments with clear speech but often misses important visual events that are not explicitly mentioned in the audio. How can you improve the system to better incorporate visual cues into the summarization process? (Select all that apply)
Answer: B,E
Explanation:
Training a separate model for visual event detection helps explicitly identify important visual cues. A multimodal attention mechanism allows the model to dynamically weigh the importance of visual and audio features. Increasing the weight of audio features would exacerbate the problem. While fine-tuning the audio model is helpful, it doesn't address the core issue of incorporating visual cues. Option E is incorrect as B is incorrect.
NEW QUESTION # 38
You are building a text-to-image application using CLIP. You notice that the generated images often lack specific details mentioned in the text prompt. Which of the following techniques would be most effective in improving the fidelity and detail of the generated images, given the limitations of CLIP's text encoder?
Answer: C
Explanation:
Prompt engineering is the most practical and effective method for improving the fidelity of text-to-image generation with CLIP, without requiring extensive retraining or architecture changes. By carefully crafting and refining the text prompt, you can guide the generation process to produce images that more accurately reflect the desired details. Training a custom text encoder (A) is resource-intensive. While a larger image decoder (B) might help, it doesn't address the core issue of accurately capturing the prompt's meaning. Increasing temperature (D) can add randomness but not necessarily detail. Reducing training steps (E) could worsen performance.
NEW QUESTION # 39
Consider this Python code snippet using PyTorch:
Answer: A
Explanation:
The shape of the 'attention' tensor is torch.Size([32, 32]). The matrix multiplication of (32, 256) with (512, 32) results in a (32, 32) tensor. The crucial issue here is the batch-wise attention calculation. The attention weights are being computed between all text embeddings and all image embeddings in the batch. During training, this leads to 'information leakage' because the model is learning relationships between samples that shouldn't be related (i.e., different text-image pairs in the batch are influencing each other). For proper cross-modal attention, you would typically want to compute the attention weights only between corresponding text and image embeddings within the same sample.
NEW QUESTION # 40
You're developing a multimodal system that takes an image and a short audio clip as input and generates a relevant story. You've trained the model, but you observe that the generated stories tend to heavily favor the content of the audio clip, largely ignoring the image. Which of the following techniques could you employ to better balance the influence of both modalities?
Answer: C
Explanation:
Modality-specific scaling factors and attention mechanisms provide a way to explicitly control the influence of each modality Increasing the learning rate of the image encoder might help, but it's not a direct solution- Reducing audio embedding dimensionality could reduce its influence, but might also lose important information. A more diverse dataset is always good, but doesn't guarantee balanced influence. Adding more layers to the audio encoder might increase its dominance.
NEW QUESTION # 41
Consider a scenario where you want to use a Transformer model for generating music. Which of the following modifications to the standard Transformer architecture would be most beneficial for capturing the long-range dependencies and musical structure inherent in music?
Answer: E
Explanation:
Relative positional embeddings are particularly effective for capturing relationships based on relative positions, which is crucial in music where the intervals and relationships between notes are significant. Standard positional embeddings only encode absolute position. Capturing those relationships greatly improves model accuracy While the other options might change behavior, they wouldn't specifically improve sequence modeling ability.
NEW QUESTION # 42
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