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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Trustworthy AI | 5% | - Ethical considerations and responsible use - Robustness and error mitigation - Reliability, fairness, and safety in generative systems |
| Topic 2: Experimentation | 25% | - Metrics and validation strategies for generative models - Experiment design and methodology - Model training, fine-tuning, and evaluation |
| Topic 3: Data Analysis and Visualization | 10% | - Visualization techniques for model behavior and results - Interpretation of generative AI outputs - Analyzing multimodal datasets and outputs |
| Topic 4: Multimodal Data | 15% | - Data preprocessing, fusion, and representation - Multimodal model architectures and integration - Characteristics of text, image, and audio data |
| Topic 5: Core Machine Learning and AI Knowledge | 20% | - Fundamental concepts of machine learning and deep learning - Generative AI principles and techniques - Neural network architectures relevant to multimodal systems |
| Topic 6: Performance Optimization | 10% | - Model efficiency and inference optimization - Hardware acceleration with NVIDIA platforms - Scalability and deployment considerations |
| Topic 7: Software Development and Engineering | 15% | - Development workflows for generative AI applications - Libraries, frameworks, and tools for multimodal AI - Best practices for building and maintaining systems |
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NEW QUESTION # 17
You are tasked with creating a multimodal AI application that analyzes social media posts containing text, images, and user profile information to predict the likelihood of a post going viral. Which feature engineering techniques are most effective for representing and integrating these different modalities?
Answer: A
Explanation:
Using word embeddings captures semantic meaning in text. Pre-trained CNN features provide high-level image representations. Graph embedding for user profiles captures relationships between users. These advanced techniques provide better representations than simple methods like TF-IDF, pixel values, or bag-of-words.
NEW QUESTION # 18
You are building a system that uses text and images to generate 3D models. The text describes the object, and the images provide visual details. During training, you observe that the model heavily relies on the image input and largely ignores the text description. What technique can you employ to encourage the model to give more weight to the textual input?
Answer: B,E
Explanation:
Explanation:A
Applying a higher dropout rate (B) to the image embedding forces the model to rely less on the image features. Curriculum learning (E) allows the model to first learn to associate simpler text descriptions with corresponding visual features, then gradually more complex descriptions are introduced.
NEW QUESTION # 19
You are developing a multimodal AI model that processes both text and images to classify news articles as either 'reliable' or 'unreliable'. After training, you notice that the model performs well on articles with strong visual cues (e.g., professionally edited images), but struggles with articles that have only text or low-quality images. Which of the following techniques would be MOST effective in improving the model's robustness and generalizability across different types of news articles?
Answer: B
Explanation:
Modality dropout forces the model to learn robust representations from each modality independently, making it less reliant on the presence of both modalities. This improves performance when one modality is missing or of low quality. Training only on high-quality images (A) would exacerbate the problem. Reducing the image modality's weight (C) might help slightly but doesn't fundamentally address the issue. Using a simpler image model (D) would likely decrease overall performance. Increasing the training dataset size with only high-quality images (E) will not address the problem of the model's dependence on high-quality images.
NEW QUESTION # 20
You are working with a multimodal model that combines text and image inputs. You want to analyze the model's attention mechanisms to understand which parts of the image are most relevant to specific words in the input text. What technique can you use to visualize and interpret the model's attention weights in this scenario?
Answer: B
Explanation:
Attention heatmaps are a visualization technique used to highlight the regions of an image that the model is focusing on when processing specific words in the input text. By overlaying the attention weights onto the image, you can identify the most relevant areas. t-SNE and PCA are dimensionality reduction techniques used for visualizing high-dimensional data in lower dimensions. ROC curves and confusion matrices are used to evaluate the performance of classification models.
NEW QUESTION # 21
What characteristic of autoencoders makes them suitable for anomaly detection?
Answer: B
Explanation:
An autoencoder learns to compress input data into a lower-dimensional latent (bottleneck) representation via its encoder, then reconstruct the original input from that representation via its decoder, trained by minimizing reconstruction error on normal data. Because the model is optimized specifically to reconstruct patterns it has seen frequently during training, it becomes proficient at compressing and reconstructing "normal" instances but performs poorly - producing high reconstruction error - on inputs that deviate structurally from the training distribution, i.e., anomalies. Thresholding reconstruction error thus provides a natural, unsupervised anomaly score without requiring labeled anomalous examples, which are often scarce or unavailable in real- world settings.
This mechanism is the operative characteristic tested here, not classification accuracy (B, which describes a supervised discriminative task the autoencoder is not directly trained for), image enhancement (C, a description closer to denoising autoencoders' side effect rather than the core anomaly-detection mechanism), or forecasting (D, which describes sequence models like RNNs/LSTMs applied to time series, a different architecture family and objective).
Variants such as variational autoencoders (VAEs) extend this idea probabilistically, and in multimodal settings, cross-modal autoencoders can flag anomalies where reconstruction fails to reconcile one modality given another.
Reference: Core Machine Learning and AI Knowledge domain - autoencoders, latent representations, reconstruction-error-based anomaly detection.
NEW QUESTION # 22
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