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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Trustworthy AI | 5% | - Robustness and error mitigation - Ethical considerations and responsible use - Reliability, fairness, and safety in generative systems |
| Topic 2: Core Machine Learning and AI Knowledge | 20% | - Generative AI principles and techniques - Fundamental concepts of machine learning and deep learning - Neural network architectures relevant to multimodal systems |
| Topic 3: Experimentation | 25% | - Metrics and validation strategies for generative models - Model training, fine-tuning, and evaluation - Experiment design and methodology |
| Topic 4: Data Analysis and Visualization | 10% | - Analyzing multimodal datasets and outputs - Interpretation of generative AI outputs - Visualization techniques for model behavior and results |
| Topic 5: Multimodal Data | 15% | - Data preprocessing, fusion, and representation - Multimodal model architectures and integration - Characteristics of text, image, and audio data |
| Topic 6: Performance Optimization | 10% | - Scalability and deployment considerations - Hardware acceleration with NVIDIA platforms - Model efficiency and inference optimization |
| Topic 7: 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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질문 # 51
You are training a Generative Adversarial Network (GAN) to generate realistic images. After several epochs, you observe that the generator is consistently producing similar images, regardless of the input noise. This phenomenon is known as:
정답：E
설명：
Mode collapse occurs when the generator in a GAN learns to produce a limited variety of outputs, often focusing on the most easily generated examples. This leads to a lack of diversity in the generated images. Vanishing and exploding gradients are related to the training process itself, overfitting relates to model complexity compared to the training data, and underfitting relates to insufficient model capacity.
질문 # 52
You are building a conditional GAN (cGAN) to generate images conditioned on text descriptions. The generator takes a noise vector and a text embedding as input. Which of the following approaches would be most effective for combining the noise vector and text embedding before feeding them into the generator's first layer?
정답：D
설명：
Applying learned linear transformations before concatenation allows the model to learn optimal projections of both the noise vector and text embedding into a shared space, improving the generator's ability to incorporate the conditioning information effectively. Directly concatenating can work, but may not be as effective as learned transformations. Other options are less common and less directly applicable to the task.
질문 # 53
You are tasked with creating a multimodal A1 assistant that can understand and respond to user queries based on images and text. The assistant should be able to identify objects in images, understand the relationships between them, and answer questions about the image content using natural language. Given a scenario where a user uploads an image of a living room and asks, 'What is the color of the sofa next to the window?', what are the essential steps and techniques needed to implement this functionality?
정답：E
설명：
All steps are required. Object detection identifies the objects, relationship extraction understands their spatial relationships, and VQA generates the natural language answer based on the image and question. Sentiment analysis is not relevant in this scenario.
질문 # 54
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?
정답：C
설명：
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.
질문 # 55
You are working with a transformer-based multimodal model that processes both text and audio. You want to implement an efficient attention mechanism that reduces the computational cost associated with attending to the entire input sequence. Which of the following attention mechanisms would be MOST suitable for achieving this goal?
정답：C
설명：
Sparse attention is designed to reduce the computational cost of attention by only attending to a subset of the input sequence at each position. This can significantly reduce the memory and computational requirements, making it suitable for long sequences in multimodal tasks. Other attention mechanisms (A, B, D, E) still require computing attention scores for all or most of the input sequence.
질문 # 56
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