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| Section | Weight | Objectives |
|---|---|---|
| Experimentation | 25% | - Metrics and validation strategies for generative models - Model training, fine-tuning, and evaluation - Experiment design and methodology |
| Multimodal Data | 15% | - Characteristics of text, image, and audio data - Multimodal model architectures and integration - Data preprocessing, fusion, and representation |
| Data Analysis and Visualization | 10% | - Interpretation of generative AI outputs - Analyzing multimodal datasets and outputs - Visualization techniques for model behavior and results |
| Performance Optimization | 10% | - Scalability and deployment considerations - Model efficiency and inference optimization - Hardware acceleration with NVIDIA platforms |
| Software Development and Engineering | 15% | - Libraries, frameworks, and tools for multimodal AI - Development workflows for generative AI applications - Best practices for building and maintaining systems |
| Trustworthy AI | 5% | - Reliability, fairness, and safety in generative systems - Ethical considerations and responsible use - Robustness and error mitigation |
| 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 # 36
You have developed a multimodal generative A1 model that generates images based on textual descriptions. You want to set up an automated system to monitor the model's performance and identify potential issues like degradation in image quality or introduction of biases over time. Which of the following components are essential for such a monitoring system? (Select THREE)
Answer: A,B,C
Explanation:
Automated metrics calculation (B) and alerting mechanisms (C) are crucial for continuously monitoring performance and detecting issues. Storing and analyzing text prompts (E) can help identify patterns related to performance degradation or bias. While storing training data (A) can be useful, it's not essential for monitoring. A manual evaluation interface (D) can be helpful for occasional spot-checks, but it's not a core component of an automated monitoring system.
NEW QUESTION # 37
You are building a Generative A1 model that generates captions for images. You want to evaluate the quality of the generated captions.
Which evaluation metrics are MOST suitable for this task?
Answer: B
Explanation:
BLEU, ROUGE, and CIDEr are standard metrics used for evaluating the quality of generated text, particularly in image captioning and machine translation. These metrics compare the generated captions to reference captions and measure the similarity in terms of n-grams, word overlap, and other features. Other options are used for Classification problems (Accuracy Precision, Fl-score, AUC) and Regression Problems (MSE, RMSE).
NEW QUESTION # 38
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 # 39
Which technique involves leveraging pre-trained models to achieve efficient results with less data and computation?
Answer: B
Explanation:
Transfer learning takes a model already trained on a large, general-purpose dataset (e.g., ImageNet for vision, or a large text corpus for language models) and adapts it to a new, typically smaller and more specific target task - either by fine-tuning some or all of the pretrained weights, or by freezing the pretrained backbone and training only new task-specific layers on top. Because the pretrained model has already learned general- purpose, reusable features (edge and texture detectors in early CNN layers, syntactic and semantic structure in language model layers), the target task requires substantially less labeled data and less compute than training a comparable model from random initialization.
Prompt engineering (C) is a related but distinct technique specific to large language and generative models: it adapts a *frozen* pretrained model's behavior through the design of the input prompt alone, without any weight updates - a lighter-weight technique than transfer learning, applicable only where a sufficiently capable pretrained model already exists. Options A and D are not standard, well-defined ML techniques matching this description; "state management and composition" and "neural network integration" are generic software-engineering-sounding terms without a specific technical meaning in this context, making them straightforward distractors to eliminate.
Reference: Core Machine Learning and AI Knowledge domain - transfer learning, fine-tuning, pretrained backbones.
NEW QUESTION # 40
You are analyzing the latent space of a GAN trained to generate images of human faces. You notice that interpolating between two points in the latent space often results in unrealistic or distorted faces. Which of the following techniques could potentially improve the smoothness and interpretability of the latent space?
Answer: C
Explanation:
Regularizing the latent space directly encourages smoothness, making interpolations more realistic. Spectral normalization in the discriminator improves training stability but doesn't directly address latent space smoothness. Increasing discriminator layers or decreasing generator learning rate might influence performance, but regularization is the most direct approach. Batch size is less impactful on latent space interpretability.
NEW QUESTION # 41
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