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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Analysis and Visualization | 10% | - Interpretation of generative AI outputs - Analyzing multimodal datasets and outputs - Visualization techniques for model behavior and results |
| Topic 2: Multimodal Data | 15% | - Multimodal model architectures and integration - Characteristics of text, image, and audio data - Data preprocessing, fusion, and representation |
| Topic 3: Trustworthy AI | 5% | - Robustness and error mitigation - Ethical considerations and responsible use - Reliability, fairness, and safety in generative systems |
| Topic 4: Core Machine Learning and AI Knowledge | 20% | - Generative AI principles and techniques - Fundamental concepts of machine learning and deep learning - Neural network architectures relevant to multimodal systems |
| Topic 5: Performance Optimization | 10% | - Model efficiency and inference optimization - Hardware acceleration with NVIDIA platforms - Scalability and deployment considerations |
| Topic 6: Experimentation | 25% | - Metrics and validation strategies for generative models - Experiment design and methodology - Model training, fine-tuning, and evaluation |
| Topic 7: Software Development and Engineering | 15% | - Best practices for building and maintaining systems - Development workflows for generative AI applications - Libraries, frameworks, and tools for multimodal AI |
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NEW QUESTION # 28
You are working on a project to classify images of different types of flowers. You have a relatively small dataset (around 500 images per class). Which of the following techniques would be the MOST effective to improve the performance of your image classifier, considering the limited data?
Answer: C
Explanation:
Transfer learning, specifically fine-tuning a pre-trained model, is highly effective when dealing with small datasets. Pre-trained models have already learned useful features from large datasets, and fine-tuning them allows the model to adapt to the specific characteristics of your flower dataset. Training a deep network from scratch with limited data will likely lead to overfitting. Data augmentation helps, but transfer learning is generally more impactful. Reducing image resolution might lose important details, and a linear classifier might be too simple to capture the complexity of image features.
NEW QUESTION # 29
You are working on a project that involves generating realistic images of furniture based on textual descriptions. The input data consists of text descriptions and a small dataset of existing furniture images. Which data augmentation techniques would be MOST effective in improving the quality and diversity of the generated images?
Answer: D
Explanation:
Combining all techniques provides the best results. Image augmentations like cropping and rotation increase the variance of the image data. GANs create entirely new images, and text augmentation enhances the diversity of the input descriptions. Focusing only on one modality will likely limit the model's performance.
NEW QUESTION # 30
Consider the following Python code snippet used for evaluating a generative model. What potential issue exists with this code, and how would you rectify it to ensure a robust evaluation?
Answer: D
Explanation:
Generative models produce different outputs each time due to their inherent stochasticity. Evaluating only once can result in a score that isn't representative. The code needs to be run multiple times with different random seeds, and the results averaged to obtain a more reliable Inception Score. While FID is a good metric (A), the core issue here is the lack of accounting for stochasticity. While a larger sample size can help improve reliability, addressing the randomness is most important. Inception score is a valid metric (D) although it has limitations. The given sample code is assumed to be incomplete, hence it will not have any issue on its own.
NEW QUESTION # 31
You're developing a multimodal system that takes an image and a short audio clip as input and generates a relevant story. You've trained the model, but you observe that the generated stories tend to heavily favor the content of the audio clip, largely ignoring the image. Which of the following techniques could you employ to better balance the influence of both modalities?
Answer: E
Explanation:
Modality-specific scaling factors and attention mechanisms provide a way to explicitly control the influence of each modality Increasing the learning rate of the image encoder might help, but it's not a direct solution- Reducing audio embedding dimensionality could reduce its influence, but might also lose important information. A more diverse dataset is always good, but doesn't guarantee balanced influence. Adding more layers to the audio encoder might increase its dominance.
NEW QUESTION # 32
You are evaluating the performance of an AI model for facial recognition. What is an important consideration when evaluating the model for bias?
Answer: B
Explanation:
Bias evaluation for facial recognition centers on measuring whether the model's accuracy - true positive rate, false positive rate, false match rate - is consistent across demographic subgroups (race, gender, age), rather than assuming a single aggregate accuracy figure represents performance fairly for all groups. This concern is well grounded empirically: independent benchmarking, including NIST's Face Recognition Vendor Test studies, has repeatedly documented substantial accuracy disparities across demographic groups in widely deployed facial recognition systems, with error rates for some subgroups measured many times higher than for others - a direct consequence of training data underrepresentation and the representativeness-bias issue covered elsewhere in this domain.
Processing speed (A) is a performance/latency engineering concern, not a bias or fairness concern - a model could process all demographic groups at identical speed while still exhibiting severe accuracy disparities between them. Facial expression recognition capability (C) addresses a different task dimension (emotion
/expression classification) than the identity-recognition bias question being asked. Operating system compatibility (D) is a software-deployment/engineering concern entirely unrelated to model fairness.
Subgroup accuracy disparities in facial recognition carry serious real-world consequences - misidentification risk in law enforcement or access-control contexts - which is why disaggregated (per-subgroup) evaluation, not just aggregate accuracy, is considered a baseline requirement under Trustworthy AI bias auditing practice.
Reference: Trustworthy AI domain - subgroup/disaggregated bias evaluation, fairness auditing.
NEW QUESTION # 33
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