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| Section | Weight | Objectives |
|---|---|---|
| Multimodal Data | 15% | - Characteristics of text, image, and audio data - Data preprocessing, fusion, and representation - Multimodal model architectures and integration |
| Software Development and Engineering | 15% | - Libraries, frameworks, and tools for multimodal AI - Best practices for building and maintaining systems - Development workflows for generative AI applications |
| Trustworthy AI | 5% | - Reliability, fairness, and safety in generative systems - Robustness and error mitigation - Ethical considerations and responsible use |
| Core Machine Learning and AI Knowledge | 20% | - Generative AI principles and techniques - Neural network architectures relevant to multimodal systems - Fundamental concepts of machine learning and deep learning |
| Performance Optimization | 10% | - Hardware acceleration with NVIDIA platforms - Scalability and deployment considerations - Model efficiency and inference optimization |
| Data Analysis and Visualization | 10% | - Analyzing multimodal datasets and outputs - Visualization techniques for model behavior and results - Interpretation of generative AI outputs |
| Experimentation | 25% | - Model training, fine-tuning, and evaluation - Metrics and validation strategies for generative models - Experiment design and methodology |
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NEW QUESTION # 30
You're tasked with building a system that generates personalized exercise recommendations based on user's text descriptions of their fitness goals and images of their current physical condition. Due to privacy concerns, you cannot directly access the user's raw images or text after initial processing. What technique can allow you to continue to train the model while respecting these privacy constraints?.
Answer: E
Explanation:
Federated learning allows training a model across multiple decentralized devices or servers holding local data samples, without exchanging them. This is perfect for privacy-sensitive scenarios as the raw data remains on the user's device. Transfer learning relies on pre- trained models, data augmentation modifies existing data, GANs generate new data (but still require initial data access), and reinforcement learning optimizes actions through interaction with an environment.
NEW QUESTION # 31
You want to evaluate the performance of an AI model. Which of the following is a method for AI model evaluation?
Answer: B
Explanation:
Valid model evaluation requires measuring performance on held-out data the model has not seen during training - this is the foundational principle behind train/validation/test splits and cross-validation, and it exists specifically to estimate how the model will generalize to genuinely new data, rather than how well it memorized patterns specific to its training set. Option B correctly describes this: sampling from a portion of the dataset explicitly excluded from training and calculating accuracy on it.
Options C and D both violate this principle by evaluating on the training set itself, which produces optimistically biased performance estimates: a model - particularly an overparameterized deep learning model - can achieve very high training accuracy or very low training loss simply by memorizing training examples (overfitting) without that performance transferring to new data at all. Reporting training-set accuracy (C) or training-set loss (D) as an evaluation of "performance" conflates fit-to-training-data with generalization, the central failure mode that held-out evaluation is designed to catch. Option A describes a qualitative, subjective process - interviewing developers - that provides no quantitative, reproducible performance measurement and is not a recognized model evaluation methodology.
This principle extends further in rigorous experimentation: a validation set used repeatedly for hyperparameter tuning can itself become "leaked" through repeated selection, which is why a separate, untouched test set is typically reserved for final, one-time performance reporting.
Reference: Experimentation domain - held-out evaluation, train/validation/test methodology, avoiding overfitting bias in reported metrics.
NEW QUESTION # 32
You are working on a project that involves generating realistic images from text descriptions using a diffusion model. You want to reduce the inference time of the model, which currently takes several minutes to generate a single image. Which of the following techniques would be MOST effective for accelerating inference without significantly compromising image quality?
Answer: B
Explanation:
DDIM and progressive distillation are specifically designed to reduce the number of sampling steps needed in diffusion models, leading to faster inference. Increasing diffusion steps (A) would increase inference time. Smaller batch size (B) might reduce memory usage, but not significantly affect overall inference time. Training with a larger dataset (D) improves quality, but not inference speed. Switching to CPU (E) would dramatically slow down inference.
NEW QUESTION # 33
You are building an image generation pipeline that leverages both a U-Net and a pre-trained CLIP model. After generating an image with the U-Net, you want to use CLIP to assess how well the generated image aligns with a given text prompt. Which of the following steps are crucial for obtaining a meaningful similarity score between the image and the text using CLIP?
Answer: C,D,E
Explanation:
To assess the alignment between a generated image and a text prompt using CLIP, you need to encode both the image and the text into vector representations using CLIP's respective encoders (image and text encoders). Then, calculate the cosine similarity between these embeddings to quantify their semantic relatedness. Fine-tuning CLIP is not typically necessary for this purpose. High resolution is not mandatory as CLIP works well on medium resolution images and it's embedded space.
NEW QUESTION # 34
You're working with a text-to-image generation model. After training, you notice the generated images lack fine-grained details and appear blurry. Which hyperparameter tuning strategy would be MOST effective in improving the visual quality of the generated images, considering the computational cost?
Answer: A
Explanation:
Optimizing the learning rate schedule can have a significant impact on the quality of the generated images. A well-tuned learning rate can help the model converge to a better solution and avoid getting stuck in local minima. Increasing the number of training epochs may help, but also increases computational cost and can lead to overfitting. Adding more layers to the discriminator is a valid approach to consider if using GANs. While switching to a different architecture is an option, it would need to be justified by experimental results and may have other implications.
NEW QUESTION # 35
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