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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Multimodal Data | 15% | - Multimodal model architectures and integration - Characteristics of text, image, and audio data - Data preprocessing, fusion, and representation |
| Topic 2: Trustworthy AI | 5% | - Robustness and error mitigation - Reliability, fairness, and safety in generative systems - Ethical considerations and responsible use |
| Topic 3: Data Analysis and Visualization | 10% | - Analyzing multimodal datasets and outputs - Visualization techniques for model behavior and results - Interpretation of generative AI outputs |
| Topic 4: Software Development and Engineering | 15% | - Development workflows for generative AI applications - Best practices for building and maintaining systems - Libraries, frameworks, and tools for multimodal AI |
| Topic 5: Performance Optimization | 10% | - Hardware acceleration with NVIDIA platforms - Scalability and deployment considerations - Model efficiency and inference optimization |
| Topic 6: Experimentation | 25% | - Metrics and validation strategies for generative models - Model training, fine-tuning, and evaluation - Experiment design and methodology |
| Topic 7: Core Machine Learning and AI Knowledge | 20% | - Fundamental concepts of machine learning and deep learning - Neural network architectures relevant to multimodal systems - Generative AI principles and techniques |
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NEW QUESTION # 35
You are building a multimodal emotion recognition system that takes both facial expressions (images) and speech audio as input. During development, you observe that the model is heavily biased towards the audio modality, effectively ignoring the visual input. Which technique would be the LEAST effective in mitigating this modality bias?
Answer: A
Explanation:
Increasing the audio branch's complexity while simplifying the image branch would actually exacerbate the modality bias towards audio. The other techniques (modality dropout, gradient blending, loss reweighting, and adversarial training) are all strategies designed to encourage the model to utilize both modalities more evenly. Increasing the model parameters of one mode leads to over-representation of that mode.
NEW QUESTION # 36
You are optimizing a multimodal model for deployment on an edge device with limited memory and computational resources. The model takes video frames and audio as input to predict human actions. Which of the following optimization techniques would provide the MOST significant reduction in model size and computational complexity without drastically sacrificing accuracy?
Answer: B
Explanation:
Knowledge distillation involves training a smaller 'student' model to mimic the behavior of a larger, more complex 'teacher' model. This can significantly reduce the size and computational complexity of the model while preserving a reasonable level of accuracy. Increasing the number of layers or using larger kernel sizes would increase complexity. Increasing audio sampling rate would increase the input data size. Removing batch normalization might reduce computational complexity slightly, but could negatively impact accuracy.
NEW QUESTION # 37
In machine learning, what is the purpose of data normalization?
Answer: A
Explanation:
Normalization rescales numeric features onto a common, well-defined range or distribution - for example, min-max scaling to [0,1], or standardization to zero mean and unit variance (z-score) - so that features measured on different scales contribute comparably to model training. Among the options given, "converting data into a specific format for easier analysis" is the closest description of this rescaling purpose, though the more precise technical framing is: normalization standardizes the scale of feature values to stabilize and accelerate optimization.
This matters mechanically because many algorithms are scale-sensitive: gradient descent converges faster and more stably when input features share a comparable range (large-scale features would otherwise dominate the loss gradient), distance-based methods (k-NN, k-means, SVMs with RBF kernels) require comparable scales for distance calculations to be meaningful, and regularization terms penalize weight magnitude uniformly, which only makes sense if inputs are on comparable scales.
It is important to distinguish normalization from the other three options: it does not remove data (A, which is cleansing/filtering), does not increase complexity (B, the opposite of its intent), and does not reduce dimensionality (D, which describes techniques like PCA or feature selection - an entirely separate preprocessing goal focused on the number of features, not their scale).
Reference: Core Machine Learning and AI Knowledge domain - feature scaling (normalization, standardization) vs. dimensionality reduction.
NEW QUESTION # 38
Which of the following are key architectural features of a U-Net that make it suitable for image generation tasks, particularly when starting from pure noise?
Answer: B,C,D,E
Explanation:
U-Nets excel at image generation due to their skip connections, convolutional layers, bottleneck layer, and upsampling techniques. Skip connections preserve fine-grained details from earlier encoder layers during decoding. Convolutional layers extract spatial features. The bottleneck compresses information, and upsampling reconstructs the image to the original resolution. Fully connected layers are generally not used for image generation but for classification.
NEW QUESTION # 39
You're building a system to translate speech to text using an encoder-decoder architecture with attention. You observe that the translated text often repeats phrases from the input speech. Which regularization techniques could help mitigate this issue? (Select TWO)
Answer: A,B
Explanation:
Dropout prevents overfitting by randomly dropping neurons during training, forcing the network to learn more robust representations and reducing reliance on specific features. Label smoothing encourages the model to be less confident in its predictions, making it less likely to overfit to the training data and repeat phrases. The size of the vocabulary, the number of attention heads, and L1 regularization on embeddings are less directly related to preventing repetition.
NEW QUESTION # 40
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