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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Performance Optimization | 10% | - Scalability and deployment considerations - Hardware acceleration with NVIDIA platforms - Model efficiency and inference optimization |
| Topic 2: Data Analysis and Visualization | 10% | - Interpretation of generative AI outputs - Analyzing multimodal datasets and outputs - Visualization techniques for model behavior and results |
| Topic 3: Trustworthy AI | 5% | - Reliability, fairness, and safety in generative systems - Robustness and error mitigation - Ethical considerations and responsible use |
| Topic 4: Software Development and Engineering | 15% | - Development workflows for generative AI applications - Best practices for building and maintaining systems - Libraries, frameworks, and tools for multimodal AI |
| Topic 5: Multimodal Data | 15% | - Characteristics of text, image, and audio data - Multimodal model architectures and integration - Data preprocessing, fusion, and representation |
| Topic 6: Experimentation | 25% | - Metrics and validation strategies for generative models - Model training, fine-tuning, and evaluation - Experiment design and methodology |
| Topic 7: Core Machine Learning and AI Knowledge | 20% | - Fundamental concepts of machine learning and deep learning - Neural network architectures relevant to multimodal systems - Generative AI principles and techniques |
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NEW QUESTION # 16
Consider the following code snippet which aims to create a custom prompt for a Stable Diffusion model using the 'diffusers* library. The goal is to generate an image of 'a cat wearing a hat sitting on a chair'. Which of the following modifications would MOST effectively improve the quality and coherence of the generated image?
Answer: B
Explanation:
Increasing the 'guidance_scales forces the generated image to adhere more closely to the prompt. Adding a negative prompt helps remove unwanted artifacts and improves image quality. Negative prompt is a well know prompt engginerring technique to remove unwanted objects or attributes.
NEW QUESTION # 17
Consider a multimodal emotion recognition system that uses both facial expressions and speech audio as input. You want to fuse the information from these two modalities. Which of the following fusion techniques would be most suitable if the modalities have significantly different temporal resolutions (e.g., facial expressions change more rapidly than overall vocal tone)?
Answer: D
Explanation:
Intermediate fusion, particularly with attention mechanisms, is well-suited for modalities with different temporal resolutions. Attention allows the model to dynamically align and weight the features from each modality based on their relevance at different time steps, addressing the temporal misalignment issue. Early fusion would be problematic as the temporal differences are not handled. Late fusion ignores the potential interactions between the modalities. Decision fusion suffers from the same issues as late fusion. Feature extraction is not fusion technique.
NEW QUESTION # 18
You are fine-tuning a pre-trained Generative A1 model for a specific downstream task with limited dat a. The model starts to exhibit catastrophic forgetting of the original pre-trained knowledge. Which technique could mitigate this issue effectively?
Answer: A,D
NEW QUESTION # 19
You are building a multimodal emotion recognition system that uses facial expressions (images) and speech (audio). You want to use transfer learning to leverage pre-trained models for both modalities. You have access to a large pre-trained facial recognition model (trained on millions of faces) and a large pre-trained speech recognition model (trained on thousands of hours of speech). How do you design a multimodal transfer learning strategy to efficiently train the entire system on a smaller dataset of peoples face and audio samples?
Answer: B,E
Explanation:
Fine-tuning the pre-trained models using a joint loss function helps the model to adapt to a combined face and speech emotion recognition task. In addition, using the features of one modality as an attention mechanism for the other modality can help guide an end-to-end training model. Feature extraction is more of a traditional method and does not fully allow pre-trained models to fully transfer. Training in series might not result in the best model performance since multimodal emotion recognition is about using all facets of information to predict.
NEW QUESTION # 20
You're developing a real-time multimodal A1 system that processes live video and audio streams. The system's performance is lagging behind requirements. Which of the following optimization strategies would be MOST effective in improving the system's throughput and reducing latency?
Answer: B,D,E
Explanation:
TensorRT optimizes models for NVIDIA GPUs, significantly improving inference speed and reducing latency. Mixed-precision inference reduces memory bandwidth requirements and allows for faster computations on Tensor Cores. Reducing the number of layers decreases the computational complexity of the model. RNNs are not typically faster than Transformers, especially when optimized with libraries like TensorRT. While a larger batch size can increase throughput, it also increases latency, which is detrimental for a real-time system.
NEW QUESTION # 21
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