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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Analysis and Visualization | 10% | - Analyzing multimodal datasets and outputs - Visualization techniques for model behavior and results - Interpretation of generative AI outputs |
| Topic 2: Performance Optimization | 10% | - Hardware acceleration with NVIDIA platforms - Scalability and deployment considerations - Model efficiency and inference optimization |
| Topic 3: Multimodal Data | 15% | - Characteristics of text, image, and audio data - Data preprocessing, fusion, and representation - Multimodal model architectures and integration |
| Topic 4: Trustworthy AI | 5% | - Ethical considerations and responsible use - Reliability, fairness, and safety in generative systems - Robustness and error mitigation |
| Topic 5: Software Development and Engineering | 15% | - Development workflows for generative AI applications - Libraries, frameworks, and tools for multimodal AI - Best practices for building and maintaining systems |
| Topic 6: Experimentation | 25% | - Experiment design and methodology - Model training, fine-tuning, and evaluation - Metrics and validation strategies for generative models |
| Topic 7: Core Machine Learning and AI Knowledge | 20% | - Generative AI principles and techniques - Neural network architectures relevant to multimodal systems - Fundamental concepts of machine learning and deep learning |
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NEW QUESTION # 49
When deploying a multimodal Generative A1 model for a real-time application, such as a virtual assistant that responds to voice commands and displays relevant images, which of the following considerations are MOST critical for ensuring low latency and a smooth user experience? (Select TWO)
Answer: C,E
Explanation:
Model quantization and pruning reduce the model's size and computational complexity, leading to faster inference. Asynchronous processing and caching allow for pre-computation and storage of frequently used data, minimizing delays. Prioritizing accuracy over speed (A) is not suitable for real-time applications where responsiveness is crucial. Deploying on a single CPU core (D) would severely limit performance. Disabling logging (E) is detrimental for debugging and monitoring.
NEW QUESTION # 50
You are training a multimodal model with text and audio inputs. You notice that the audio modality dominates the training process, and the text modality is not contributing significantly to the final performance. Which of the following strategies can you use to address this modality imbalance? (Select TWO)
Answer: A,E
Explanation:
Increasing the learning rate for the text encoder can help the text modality learn more effectively Applying a modality-specific weighting scheme to the loss function allows you to explicitly control the contribution of each modality to the overall loss, giving more weight to the underperforming text modality. Decreasing the batch size for audio data might have a small impact, but it's not a primary strategy for addressing modality imbalance. Removing the audio modality is not a desirable solution, as it eliminates valuable information. Increasing the size of audio dataset will even more dominate_ So, the most effective strategies are increasing the learning rate for text and weighting the loss function.
NEW QUESTION # 51
You are building a multimodal generative A1 model that combines text, images, and audio. You notice that the model performs well on text and images but struggles with audio, particularly in noisy environments. Which of the following strategies would be MOST effective in improving the model's performance with audio data?
Answer: A,C
Explanation:
Data augmentation (C) increases the robustness of the model to variations in audio, including noise. Transfer learning (E) allows the model to leverage knowledge from a large, pre-existing audio dataset, improving its initial performance.
NEW QUESTION # 52
Consider a multimodal generative A1 model that produces images based on textual prompts. The model is prone to generating images that are similar to those in the training data, resulting in a lack of novelty. Which hyperparameter adjustment would be MOST effective in increasing the diversity of the generated images?
Answer: A
Explanation:
Increasing the temperature parameter during decoding introduces more randomness into the sampling process, leading to a wider range of possible outputs and thus increasing the diversity of generated images. Decreasing batch size doesn't directly affect diversity. Reducing the learning rate affects training, not the diversity of generated images after training. Decreasing layers might reduce model capacity but not necessarily increase diversity. Increasing weight decay can prevent overfitting but doesn't directly address the lack of novelty in generated images.
NEW QUESTION # 53
You are working with time-series data from IoT sensors alongside video footage from surveillance cameras to detect anomalies in a factory production line. What data preprocessing steps are crucial for effectively integrating and analyzing these modalities in a multimodal AI model?
Answer: D
Explanation:
All the mentioned steps are crucial. Synchronizing timestamps is essential for temporal alignment. Normalizing time-series data ensures features are on the same scale, preventing bias. Downsampling video reduces computational burden, and grayscale conversion simplifies feature extraction without losing vital information for anomaly detection.
NEW QUESTION # 54
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