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| Section | Weight | Objectives |
|---|---|---|
| Multimodal Data | 15% | - Handling and integrating text, image, and audio data - Applications and use cases |
| Experimentation | 25% | - A/B testing - Hypothesis testing - Experimental design - Model evaluation and comparison |
| Core ML & AI Knowledge | 20% | - Key algorithms and techniques - Basic concepts and terminology |
| 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 |
| Performance Optimization | 10% | - Techniques for optimizing AI performance - Monitoring and improving system efficiency |
| Data Analysis & Visualization | 10% | - Visualization techniques for multimodal data - Data preprocessing and feature engineering |
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NEW QUESTION # 17
You are training a Variational Autoencoder (VAE) and notice that the generated samples are blurry and lack detail. Which of the following adjustments could help improve the quality and sharpness of the generated images2 Select all that apply.
Answer: A,C,D,E
Explanation:
Increasing network capacity allows the model to learn more complex representations. Decreasing the KL divergence allows the decoder to focus more on reconstruction, potentially sacrificing some disentanglement. Increasing the latent space provides more room for capturing variations in the data. Using a more powerful decoder helps in generating sharper images
NEW QUESTION # 18
You are tasked with optimizing a multimodal model that combines audio and text data for speech recognition. The model currently struggles with noisy audio environments. Which data augmentation technique would be MOST effective in improving the model's robustness to noise?
Answer: C
Explanation:
Adding Gaussian noise to the audio data directly simulates noisy environments, making the model more robust to such conditions. Randomly masking parts of the text input is a technique used for language modeling, and rotating images is irrelevant to audio processing. Translating the text into different languages and back is not a direct solution to noise in audio. Normalizing the text data to lowercase is more about standardization than noise robustness.
NEW QUESTION # 19
You are developing a multimodal model that processes text and time-series data to predict equipment failure. The text data consists of maintenance logs, and the time-series data consists of sensor readings. You observe that the model performs poorly when there are abrupt changes in sensor readings that are not reflected in the maintenance logs. How would you address this data integration challenge? (Select all that apply).
Answer: C,D
Explanation:
Implementing change-point detection and generating synthetic text descriptions helps bridge the gap between the two modalities. Cross-attention allows the model to dynamically integrate information from both modalities. Downsampling the time series data could lead to loss of information. Option E is incorrect because downsampling may hide crucial details in the time-series data.
NEW QUESTION # 20
You are building a system that uses audio and video to detect emotional states of a user. What are the challenges to this system?
Answer: C
Explanation:
All the options pose challenges. Background noise affects audio analysis, varying lighting impairs facial recognition, subjective emotional expression necessitates robust models, and synchronization issues hinder accurate multimodal integration.
NEW QUESTION # 21
You're working on a multimodal AI system that combines text and image dat a. You're using a contrastive learning approach to learn joint embeddings of text and images. However, you notice that the system performs well on seen image-text pairs but poorly on unseen combinations. What technique MOST directly addresses this generalization problem?
Answer: C
Explanation:
Hard negative mining focuses on selecting the most challenging negative examples (incorrect image-text pairs) during training. This forces the model to learn more robust and discriminative embeddings that generalize better to unseen combinations. Increasing embedding dimension or using larger batch size might help to some extent, but hard negative mining directly addresses the core issue of distinguishing similar but incorrect pairs. Decreasing the temperature parameter can make the contrastive loss too sensitive, potentially hindering generalization. A simpler model architecture may be detrimental if it lacks the capacity to capture the complex relationships
NEW QUESTION # 22
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