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| Section | Weight | Objectives |
|---|---|---|
| Trustworthy AI | 5% | - Ensuring fairness and transparency - Ethical considerations in AI development |
| Software Development & Engineering | 15% | - Integration and deployment of multimodal AI systems - Python libraries for multimodal AI |
| Multimodal Data | 15% | - Applications and use cases - Handling and integrating text, image, and audio data |
| Core ML & AI Knowledge | 20% | - Key algorithms and techniques - Basic concepts and terminology |
| Data Analysis & Visualization | 10% | - Visualization techniques for multimodal data - Data preprocessing and feature engineering |
| Performance Optimization | 10% | - Techniques for optimizing AI performance - Monitoring and improving system efficiency |
| Experimentation | 25% | - A/B testing - Hypothesis testing - Experimental design - Model evaluation and comparison |
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NEW QUESTION # 23
You are training a multimodal generative A1 model for image captioning. After initial training, you observe that the model excels at describing common objects but struggles with nuanced details and rare objects. Which of the following performance optimization strategies would be MOST effective in addressing this issue?
Answer: E
Explanation:
Implementing a custom loss function is the most effective strategy because it directly addresses the model's weakness by focusing on accurate descriptions of rare objects. Increasing batch size improves training speed but not necessarily accuracy. Early stopping prevents overfitting, but doesn't specifically target the issue of rare object recognition. Reducing the learning rate might help with fine-tuning, but not as effectively as a targeted loss function. Increasing the number of layers may increase complexity but not guarantee better performance on rare objects.
NEW QUESTION # 24
When working with geospatial data in conjunction with text data (e.g., analyzing tweets related to specific geographical locations), what are some of the key challenges in terms of data curation and quality assessment, and how can these challenges be addressed?
Answer: A,B,D
Explanation:
Geospatial data often suffers from inaccuracies, inconsistencies in coordinate systems, and sparsity. Addressing these challenges requires geocoding, coordinate system transformations, and spatial interpolation techniques. Many tools are available for geospatial-textual analysis.
NEW QUESTION # 25
You are working on a sequence-to-sequence model for neural machine translation. You've implemented an attention mechanism, but the model is still struggling with long sentences, often losing context in the later parts of the translation. Which type of attention mechanism is most likely to alleviate this issue effectively?
Answer: C
Explanation:
Multi-Head Attention allows the model to attend to different parts of the input sequence with different learned linear projections, capturing richer relationships and improving performance on long sequences compared to single-head attention mechanisms- While other attention mechanisms are valuable, multi- head attention offers the most robust solution for long-range dependencies.
NEW QUESTION # 26
You're training a multimodal Generative A1 model that takes video and text as input to predict future frames of the video. You notice that the model generates plausible visual content but often fails to accurately reflect the actions described in the text. Which of the following techniques is MOST likely to improve the alignment between the generated video and the text description?
Answer: D
Explanation:
Contrastive learning directly encourages the model to learn a shared representation space where semantically similar video frames and text descriptions are close to each other, improving alignment. Increasing frame rate, vocabulary size, or decreasing video resolution will not directly address the alignment problem. Training the whole model is needed instead of using just pre-trained weights.
NEW QUESTION # 27
You are fine-tuning a pre-trained large language model (LLM) for a specific text generation task. During training, you observe that the model is overfitting to the training data and not generalizing well to unseen examples. Which of the following techniques could be MOST effective in mitigating overfitting in this scenario?
Answer: A,B,C
Explanation:
Dropout regularization prevents the model from relying too heavily on specific neurons. Decreasing the learning rate reduces the step size during training, preventing the model from memorizing the training data. Early stopping prevents the model from training for too long and overfitting to the training data. While increasing the training dataset can help, it might not always be feasible. Smaller batch sizes can sometimes increase generalization, but it's less direct than the other techniques.
NEW QUESTION # 28
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