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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 2: Trustworthy AI | 5% | - Robustness and error mitigation - Ethical considerations and responsible use - Reliability, fairness, and safety in generative systems |
| Topic 3: Performance Optimization | 10% | - Hardware acceleration with NVIDIA platforms - Model efficiency and inference optimization - Scalability and deployment considerations |
| Topic 4: Experimentation | 25% | - Experiment design and methodology - Model training, fine-tuning, and evaluation - Metrics and validation strategies for generative models |
| Topic 5: Data Analysis and Visualization | 10% | - Analyzing multimodal datasets and outputs - Interpretation of generative AI outputs - Visualization techniques for model behavior and results |
| Topic 6: 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 |
| Topic 7: Multimodal Data | 15% | - Characteristics of text, image, and audio data - Data preprocessing, fusion, and representation - Multimodal model architectures and integration |
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NEW QUESTION # 41
Which of the following are valid techniques for improving the trustworthiness of a Generative A1 model used for generating product descriptions based on images, specifically addressing potential biases and ensuring fairness? (Select TWO)
Answer: B,E
Explanation:
Adversarial debiasing aims to make the model invariant to sensitive attributes, thereby reducing bias. A diverse and representative training dataset is crucial for fairness as it avoids skewed learning. Using data from a single manufacturer (A) introduces bias. Ignoring fairness metrics (D) defeats the purpose of ensuring trustworthiness. Input validation (B) is important for safety, but doesn't directly address inherent biases in the model's learned representations.
NEW QUESTION # 42
You're building a multimodal model to predict stock prices using news articles (text) and historical price data (time series). You observe that the model performs well on the training data but poorly on unseen dat a. Which of the following techniques would be MOST effective in addressing this issue?
Answer: E
Explanation:
All the listed techniques combat overfitting. Increasing data size provides more examples. Regularization penalizes complex models. Early stopping prevents training too long on the training data. Data augmentation creates more robust and generalizable model by artifically expanding data through techniques like Tme series perturbation and Text paraphrasing.
NEW QUESTION # 43
You are building a multimodal model that combines text and image data to generate captions. The text encoder is a pre-trained BERT model, and the image encoder is a ResNet-50. You observe that the generated captions are heavily biased towards descriptions based on the text input, and the image information is not well represented. Which of the following techniques could you apply to improve the contribution of the image modality?
Answer: B
Explanation:
Applying a modality-specific loss weight allows you to explicitly control the importance of each modality during training. By increasing the weight of the image loss, you encourage the model to pay more attention to the image information and generate captions that are more representative of the visual content. Increasing BERT's learning rate could worsen the imbalance. PCA is a data reduction technique not a balancing technique, freezing the weights on resnet 50, will not allow the network to learn about the relationship, batch size is to do with training speed not modality balance.
NEW QUESTION # 44
Which data augmentation techniques are MOST suitable for improving the robustness of a multimodal model that uses images and text?
Answer: A,D
Explanation:
Rotating images and back-translating text are effective as they introduce variations that the model might encounter in real-world scenarios. Adding Gaussian noise and randomly deleting words helps the model become more robust to noisy or incomplete data. Cropping and translation, while augmentation techniques, don't specifically target multimodal robustness as effectively. Changing resolution and font are less likely to generalize well.
NEW QUESTION # 45
You've trained a large multimodal model that takes text and images as input and generates creative stories. While the model produces high-quality stories in general, it occasionally generates outputs that are factually incorrect or nonsensical. Which of the following techniques would be MOST effective in improving the model's factual accuracy and coherence?
Answer: C
Explanation:
Retrieval-augmented generation (RAG) involves retrieving relevant information from an external knowledge source (e.g., a database or a collection of documents) and incorporating it into the model's generation process. This can help the model generate more factually accurate and coherent outputs by grounding its responses in external knowledge. Increasing the model size might improve performance, but doesn't guarantee factual accuracy. Reducing temperature makes the output more predictable but doesn't address factual correctness. Smaller datasets and removing dropout would likely worsen performance.
NEW QUESTION # 46
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