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| Section | Weight | Objectives |
|---|---|---|
| Experimentation | 25% | - Model evaluation and comparison - Hypothesis testing - A/B testing - Experimental design |
| Performance Optimization | 10% | - Techniques for optimizing AI performance - Monitoring and improving system efficiency |
| Data Analysis & Visualization | 10% | - Data preprocessing and feature engineering - Visualization techniques for multimodal data |
| Trustworthy AI | 5% | - Ensuring fairness and transparency - Ethical considerations in AI development |
| Core ML & AI Knowledge | 20% | - Basic concepts and terminology - Key algorithms and techniques |
| Multimodal Data | 15% | - Applications and use cases - Handling and integrating text, image, and audio data |
| Software Development & Engineering | 15% | - Python libraries for multimodal AI - Integration and deployment of multimodal AI systems |
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NEW QUESTION # 47
You're building a generative A1 model that can create realistic 3D models from text descriptions. You have a dataset of text descriptions and corresponding 3D models, but the alignment between the text and the 3D models is weak. The model sometimes generates 3D shapes that don't accurately reflect the text. Which of the following techniques could improve the alignment between the text descriptions and the generated 3D models?
Answer: D,E
Explanation:
A contrastive loss function directly encourages the model to learn a mapping between text and 3D models that preserves semantic similarity. Using a pre-trained text encoder allows the model to leverage existing knowledge about language and extract more meaningful features from the text descriptions, improving alignment. Increasing the number of vertices and faces can improve the resolution of the models but won't directly address alignment. 3D data augmentation can improve robustness, but it's less direct. Batch size has a smaller impact compared to the other options.
NEW QUESTION # 48
What advantage does multimodal learning have over unimodal learning?
Answer: D
Explanation:
Multimodal learning's principal advantage is access to complementary and, at times, redundant information across modalities that a single modality alone cannot provide - enabling the model to capture richer, more nuanced patterns and relationships. A sentiment analysis system that sees only text misses tone-of-voice cues available in audio and facial expression cues available in video; combining all three lets the model resolve ambiguity that any single modality would leave unresolved (sarcasm detected via mismatched text sentiment and vocal tone, for instance). This complementarity is the substantive, well-evidenced advantage of multimodal approaches in the research literature.
The other options overstate or misstate multimodal learning's properties: it does not inherently require fewer data samples (A) - in fact, multimodal models often require more data to learn reliable cross-modal correspondences, and can be more data-hungry in practice, particularly during pretraining. Reliability (C) is not an inherent, guaranteed property; multimodal systems introduce new failure modes, such as sensitivity to missing or corrupted modalities and to modality imbalance, that must be explicitly engineered against - reliability is not automatic. Multimodal data is also not inherently easier to collect (D); acquiring synchronized, aligned data across multiple modalities (e.g., paired audio-video-text with accurate timestamps) is typically harder and more resource-intensive than collecting a single modality.
Reference: Multimodal Data domain - complementarity of modalities, richer pattern capture.
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: A,D
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
Consider a scenario where you are building an autoencoder using a U-Net architecture. What loss function is generally considered MOST suitable for training this autoencoder, particularly when the goal is to generate high-quality images?
Answer: D
Explanation:
Mean Squared Error (MSE) loss is commonly used for training autoencoders, including those based on IJ-Net architectures, when the goal is to reconstruct images. MSE measures the average squared difference between the original and reconstructed images. While SSIM focuses on structural similarity, MSE provides a more direct pixel-wise comparison. Cross-entropy and binary cross-entropy are more suitable for classification tasks.
NEW QUESTION # 51
You are tasked with optimizing a multimodal A1 model that processes both images and text. You observe significant latency during the image encoding phase using a pre-trained ResNet50 model. Which of the following techniques would be MOST effective in reducing latency while preserving accuracy, considering energy efficiency?
Answer: B
Explanation:
Knowledge distillation involves training a smaller, more efficient model to approximate the behavior of a larger, more accurate model. This can significantly reduce latency without a major drop in accuracy. Increasing batch size (A) may increase throughput but doesn't necessarily reduce latency per image. Replacing with a larger model (C) will increase latency and power consumption. Using full precision (D) is less energy-efficient than using mixed precision or quantization. Disabling GPU acceleration (E) would drastically increase latency.
NEW QUESTION # 52
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