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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Trustworthy AI | 5% | - Ethical considerations in AI development - Ensuring fairness and transparency |
| Topic 2: Software Development & Engineering | 15% | - Python libraries for multimodal AI - Integration and deployment of multimodal AI systems |
| Topic 3: Data Analysis & Visualization | 10% | - Data preprocessing and feature engineering - Visualization techniques for multimodal data |
| Topic 4: Core ML & AI Knowledge | 20% | - Basic concepts and terminology - Key algorithms and techniques |
| Topic 5: Multimodal Data | 15% | - Applications and use cases - Handling and integrating text, image, and audio data |
| Topic 6: Experimentation | 25% | - Hypothesis testing - Experimental design - A/B testing - Model evaluation and comparison |
| Topic 7: Performance Optimization | 10% | - Monitoring and improving system efficiency - Techniques for optimizing AI performance |
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NEW QUESTION # 36
You are tasked with building a Generative A1 model to generate realistic images of outdoor scenes. The training dataset contains a large number of images with varying lighting conditions, weather conditions, and object compositions. Which data augmentation techniques would be MOST effective in improving the model's robustness and generalization ability?
Answer: D
Explanation:
Color jittering, adding Gaussian noise, and random perspective transformations simulate variations in lighting, noise, and camera angles, making the model more robust to different real-world scenarios. Random cropping and resizing are also helpful, but less impactful than changes in the color space and noise. Horizontal flipping can be useful but is limited. A fixed rotation or Vertical flipping is not a general augmentation and might harm the model's performance.
NEW QUESTION # 37
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: B,C
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 # 38
You are working on a Generative A1 Multimodal model that takes text and audio as input and generates a video. During training, you observe that the generated videos often lack coherence with the input text. What are the potential issues you would investigate? (Select THREE)
Answer: B,C,D
Explanation:
Insufficient regularization can cause overfitting and lack of generalization, leading to incoherence. A weak conditioning mechanism means the model isn't effectively using the input text to guide the video generation. A lack of diverse training examples limits the model's ability to learn the relationships between text, audio, and video. A too-powerful discriminator can lead to mode collapse, but primarily affects diversity, not necessarily coherence directly. Input audio loudness is a preprocessing issue, not a fundamental architectural problem.
NEW QUESTION # 39
You are working on a multimodal A1 model that translates spoken language from one language (e.g., English) to another (e.g., Spanish) while also generating a corresponding visual representation of the translated sentence. You have access to a large dataset of parallel spoken language and image pairs, but the image quality is highly variable. Some images are clear and detailed, while others are blurry and noisy. How should you best handle the data to build the multimodal system?
Answer: C,D
Explanation:
Using a pre-trained image enhancement model (C) attempts to clean up the noisy images. Training using curriculum learning approach (D) is often suitable for the low-quality images. Simply discarding data would reduce the size of your dataset which can hurt performance. Applying high weight to loss function (B) and training with only high quality images (A) isn't sufficient. Ignoring data will reduce the number of modalities.
NEW QUESTION # 40
You are developing a ML model for image classification. You have a dataset with 10,000 images of cats, dogs and birds. Which of the following ML models would be the most appropriate choice for this task?
Answer: C
Explanation:
CNNs are the standard architecture for image classification because their convolutional layers exploit the spatial locality and translation invariance inherent to image data: learned filters detect local patterns (edges, textures, shapes) that compose hierarchically into higher-level features (parts, objects) as depth increases, without requiring the manual feature engineering that traditional models would need to reach comparable accuracy on raw pixel data. Pooling layers further provide a degree of spatial invariance, and parameter sharing across the image keeps the model tractable relative to a fully connected network operating on raw pixels.
Logistic Regression (A) is a linear classifier that operates on flattened feature vectors; applied directly to raw pixels of a 3-class image problem, it cannot capture the non-linear spatial structure needed to separate cats, dogs, and birds reliably, though it could serve as a baseline or as the final classification head atop CNN- extracted features. K-Means (B) is an unsupervised clustering algorithm - inappropriate here because the task is supervised classification with labeled classes. Linear Regression (C) predicts continuous outputs and is not designed for categorical class prediction at all.
For 10,000 labeled images, a CNN (potentially fine-tuned from a pretrained backbone via transfer learning, given the modest dataset size) is the appropriate and industry-standard choice.
Reference: Core Machine Learning and AI Knowledge domain - CNN architecture for image classification.
NEW QUESTION # 41
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