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| Section | Weight | Objectives |
|---|---|---|
| Trustworthy AI | 5% | - Robustness and error mitigation - Ethical considerations and responsible use - Reliability, fairness, and safety in generative systems |
| Performance Optimization | 10% | - Model efficiency and inference optimization - Hardware acceleration with NVIDIA platforms - Scalability and deployment considerations |
| Multimodal Data | 15% | - Characteristics of text, image, and audio data - Data preprocessing, fusion, and representation - Multimodal model architectures and integration |
| Data Analysis and Visualization | 10% | - Analyzing multimodal datasets and outputs - Visualization techniques for model behavior and results - Interpretation of generative AI outputs |
| Experimentation | 25% | - Model training, fine-tuning, and evaluation - Experiment design and methodology - Metrics and validation strategies for generative models |
| 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 |
| 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
You're developing a system to generate realistic 3D models from text descriptions. You're using a diffusion model-based approach and find that the generated models often lack fine details and exhibit artifacts. Which of the following techniques would likely lead to the MOST significant improvement in the quality of the generated 3D models?
Answer: E
Explanation:
Each of the above options address the lack of fine details and exhibit artifacts. Increasing diffusion steps lets the model refine the results. Using a larger U-Net architecture increases capacity of details. Classifier-free guidance allows for generating high fidelity details by better correlating text descriptions. Training on a larger dataset enables richer context. Overall improvements allow for finer details with fewer artifacts
NEW QUESTION # 17
Which of the following are valid techniques for dealing with overfitting in a deep learning model trained on image data?
Answer: A,B,E
Explanation:
Overfitting occurs when a model learns the training data too well and performs poorly on unseen data. L1/L2 regularization penalizes large weights, preventing the model from becoming too complex. Data augmentation increases the Size and diversity of the training data, reducing overfitting. Dropout randomly deactivates neurons during training, preventing co-adaptation and improving generalization. Increasing model complexity or reducing training data would likely worsen overfitting.
NEW QUESTION # 18
In LLM evaluation, what does "zero-shot learning" refer to?
Answer: D
Explanation:
Zero-shot learning describes a model's capacity to correctly perform a task it was never explicitly trained or fine-tuned on, relying instead on knowledge and generalization ability acquired during broader pretraining.
For LLMs, this typically means the model is given only a natural-language instruction or prompt describing the task - with no task-specific labeled examples provided in the prompt at all - and is expected to produce a reasonable response by generalizing from its pretraining. This is directly analogous to CLIP's zero-shot image classification (covered elsewhere in this set): a model trained broadly can be applied to a new, specific task purely through how the task is described to it, without additional task-specific training.
Option A is a subtly incorrect paraphrase: zero-shot learning is not about the model "learning from zero examples" during a training process - it's about applying a model that was never trained for the specific task at all, at inference time. The model isn't learning in the zero-shot moment; it's generalizing from prior training. Option B misapplies "zero" to training time rather than to task-specific examples - an unrelated concept. Option C directly contradicts the definition; zero-shot specifically refers to performance *without* task-specific training, not performance *after* extensive training on that task.
Zero-shot is typically contrasted with few-shot learning, where a small number of task-specific examples are included in the prompt to guide the model's response without updating its weights.
Reference: Core Machine Learning and AI Knowledge domain - zero-shot vs. few-shot learning, generalization in LLMs.
NEW QUESTION # 19
You are building a system that identifies objects in images based on spoken commands. You have trained a model but notice that it performs poorly when the spoken command contains synonyms or paraphrases of the training data. Which of the following techniques would BEST address this issue?
Answer: D
Explanation:
Option C is the most effective solution. Word embeddings or contextual embeddings capture the semantic meaning of words and phrases, enabling the model to understand synonyms and paraphrases. Options A, B, D, and E address different issues but are not directly related to the semantic understanding of spoken commands. Increasing data size (A) might help, but semantic understanding is key. Image augmentation (B) is irrelevant to the text understanding. Simplifying commands (D) limits the system's capabilities. Reducing learning rate (E) might improve training stability but doesn't address the semantic understanding.
NEW QUESTION # 20
You are experimenting with different multimodal transformer architectures for a video understanding task. You are using a large pre- trained model and fine-tuning it on your specific dataset. You observe that the model is overfitting and struggling to generalize to unseen videos. Which of the following techniques would be most effective in mitigating overfitting in this scenario? (Choose two)
Answer: A,E
Explanation:
Weight decay and dropout are standard regularization techniques that help prevent overfitting. Data augmentation increases the diversity of the training data, improving the model's ability to generalize. Reducing the number of layers is a potentially viable option, but requires experimentation to achieve optimum performance.
NEW QUESTION # 21
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