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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Trustworthy AI | 5% | - Reliability, fairness, and safety in generative systems - Ethical considerations and responsible use - Robustness and error mitigation |
| Topic 2: Data Analysis and Visualization | 10% | - Interpretation of generative AI outputs - Visualization techniques for model behavior and results - Analyzing multimodal datasets and outputs |
| Topic 3: Multimodal Data | 15% | - Data preprocessing, fusion, and representation - Characteristics of text, image, and audio data - Multimodal model architectures and integration |
| 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: Experimentation | 25% | - Experiment design and methodology - Model training, fine-tuning, and evaluation - Metrics and validation strategies for generative models |
| Topic 6: 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 7: Performance Optimization | 10% | - Model efficiency and inference optimization - Hardware acceleration with NVIDIA platforms - Scalability and deployment considerations |
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NEW QUESTION # 38
You're tasked with building a generative A1 model for music composition. You have a large dataset of MIDl files, but the data is inconsistent in terms of tempo, key, and instrumentation. What are the crucial data transformation steps needed before training the model?
Answer: A,C,D,E
Explanation:
Consistent data is crucial for training a generative model. Rescaling note velocities ensures a uniform dynamic range. Transposing to a common key removes key-related variations. Normalizing tempo ensures consistent timing information. Standardizing instrumentation helps the model learn consistent relationships between notes and instruments. Converting to MP3 loses the symbolic representation of music notes which is vital for this application, so E is incorrect.
NEW QUESTION # 39
Consider the following Python code snippet using PyTorch, intended to combine image and text embeddings:
Which of the following statements regarding the output shapes of these combined embeddings are TRUE? (Select TWO)
Answer: B,E
Explanation:
torch.cat concatenates the embeddings along dimension 1, resulting in shape (32, 1024). Element-wise addition maintains the original shape (32, 512). The weighted sum is also element-wise, preserving the (32, 512) shape.
NEW QUESTION # 40
Given the following Python code snippet using Pandas, which is intended to filter rows where the 'price' column is greater than 100 and the 'quantity' column is less than 5, identify the correct approach to achieve this:
Answer: B,E
Explanation:
Option B uses the 'query' method for concise filtering. Option D correctly uses boolean indexing with the (and) operator within square brackets. Option E is syntactically incorrect because it uses the 'and' keyword, which is meant for single boolean values, not Pandas Series. Option A does logical OR which is not what we intend to filter. Option C is incorrecrt since filter method expects list of column names.
NEW QUESTION # 41
A multimodal AI model, designed to translate sign language videos into text, is consistently failing on specific gestures that involve rapid hand movements. Which optimization technique targeting temporal data processing would be MOST effective to apply?
Answer: B
Explanation:
RNNs and Transformers, especially those with attention, are designed to capture temporal dependencies in sequential data like video. Increasing frame rate (B) might help slightly, but doesn't address the fundamental issue of modeling sequential data effectively. The other options are either irrelevant or detrimental to capturing the rapid hand movements. RNNs/Transformers capture the movement itself, which is key here. Image segmentation could be a preprocessing step, but RNN/Transformer is the core optimization.
NEW QUESTION # 42
Consider a scenario where you are developing a multimodal A1 system to translate sign language videos into text. The system utilizes a CNN for processing video frames and an RNN for generating the text sequence. During evaluation, you observe that the system struggles to accurately translate signs that involve complex hand movements or subtle facial expressions. What are the MOST effective strategies to improve performance in this specific scenario? (Select TWO)
Answer: A,D
Explanation:
3D CNNs are designed to capture spatial-temporal information, which is essential for recognizing complex hand movements. Data augmentation helps the model generalize to different conditions and variations in signing style. Reducing frame rate (A) would likely worsen performance. Replacing the RNN with a feed forward network (D) would remove the ability to model sequential information, which is critical for translation. Increasing the depth of the CNN (B) might help, but a 3D CNN is more directly suited to the task.
NEW QUESTION # 43
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