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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Analysis and Visualization | 10% | - Visualization techniques for model behavior and results - Interpretation of generative AI outputs - Analyzing multimodal datasets and outputs |
| Topic 2: Performance Optimization | 10% | - Model efficiency and inference optimization - Hardware acceleration with NVIDIA platforms - Scalability and deployment considerations |
| Topic 3: Core Machine Learning and AI Knowledge | 20% | - Neural network architectures relevant to multimodal systems - Generative AI principles and techniques - Fundamental concepts of machine learning and deep learning |
| Topic 4: Software Development and Engineering | 15% | - Best practices for building and maintaining systems - Libraries, frameworks, and tools for multimodal AI - Development workflows for generative AI applications |
| Topic 5: Trustworthy AI | 5% | - Robustness and error mitigation - Reliability, fairness, and safety in generative systems - Ethical considerations and responsible use |
| Topic 6: Experimentation | 25% | - Experiment design and methodology - Metrics and validation strategies for generative models - Model training, fine-tuning, and evaluation |
| Topic 7: Multimodal Data | 15% | - Multimodal model architectures and integration - Characteristics of text, image, and audio data - Data preprocessing, fusion, and representation |
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NEW QUESTION # 11
You have a dataset containing information about sales performance for different regions in the last ten years.
Which type of data visualization would be most appropriate to compare the sales performance across regions on a year-by-year basis?
Answer: D
NEW QUESTION # 12
You are developing a multimodal generative model that takes a text description as input and generates a corresponding image. However, you notice that the generated images often lack fine-grained details and realism. Which of the following approaches could you employ to improve the quality and realism of the generated images? (Select all that apply)
Answer: A,B,C
Explanation:
Using a higher-resolution generator architecture allows the model to generate more detailed images. GANs are known for their ability to generate realistic images. A loss function that encourages the generated images to match the distribution of real images can also improve realism. Decreasing text encoder size or using a smaller dataset will hurt performance.
NEW QUESTION # 13
You are using NeMo to fine-tune a large language model for a text-to-image task. During training, you encounter a CUDA out-of-memory error, despite using mixed-precision training. What is the MOST effective strategy to reduce memory consumption and continue training without significantly degrading model performance?
Answer: B
Explanation:
Gradient checkpointing (also known as activation recomputation) trades compute for memory. It avoids storing all intermediate activations during the forward pass, instead recomputing them during the backward pass. This significantly reduces memory footprint, allowing you to train larger models or use larger batch sizes. Increasing batch size increases memory consumption. Decreasing gradient accumulation steps might help slightly but is less effective than gradient checkpointing. Switching to a smaller model or disabling mixed precision would degrade model performance.
NEW QUESTION # 14
You are fine-tuning a large pre-trained language model for a specific downstream task using a limited amount of training dat a. Which of the following techniques is MOST likely to prevent overfitting and improve the model's generalization performance?
Answer: E
Explanation:
Overfitting occurs when a model learns the training data too well and fails to generalize to unseen data. Aggressive weight decay and dropout are regularization techniques that penalize complex models and prevent them from memorizing the training data. Training from scratch with limited data will almost certainly lead to overfitting. A large learning rate can also exacerbate overfitting. While a larger batch size can improve training efficiency, it doesn't directly address overfitting.
NEW QUESTION # 15
Which of the following techniques is MOST suitable for aligning the feature spaces of text and images in a multimodal model?
Answer: C
Explanation:
Contrastive loss functions are designed to bring together the representations of similar data points (e.g., a picture and its caption) while pushing apart representations of dissimilar data points. This effectively aligns the feature spaces.
NEW QUESTION # 16
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