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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 |
| Performance Optimization | 10% | - Scalability and deployment considerations - Model efficiency and inference optimization - Hardware acceleration with NVIDIA platforms |
| Data Analysis and Visualization | 10% | - Interpretation of generative AI outputs - Visualization techniques for model behavior and results - Analyzing multimodal datasets and outputs |
| Trustworthy AI | 5% | - Robustness and error mitigation - Ethical considerations and responsible use - Reliability, fairness, and safety in generative systems |
| 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 |
| Core Machine Learning and AI Knowledge | 20% | - Neural network architectures relevant to multimodal systems - Generative AI principles and techniques - Fundamental concepts of machine learning and deep learning |
| Experimentation | 25% | - Metrics and validation strategies for generative models - Experiment design and methodology - Model training, fine-tuning, and evaluation |
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NEW QUESTION # 12
You are tasked with evaluating the performance of a generative model that produces synthetic tabular dat a. This data will be used for downstream tasks such as training a fraud detection model. Which of the following evaluation metrics and strategies are MOST appropriate for assessing the quality and utility of the generated data in this scenario? Select all that apply.
Answer: A,B,D
Explanation:
JSD measures the similarity between probability distributions, making it suitable for comparing feature distributions in the real and synthetic datasets. Training a downstream model on synthetic data and evaluating it on real data directly assesses the utility of the synthetic data. PCA visualization helps compare the overall structure and distribution of the datasets. FID and SSIM are primarily used for evaluating image generation models, not tabular data. Thus A, B and C are the only correct answers.
NEW QUESTION # 13
You are developing a multimodal model that combines time-series data from sensor readings with natural language descriptions of events. The time-series data has varying sampling rates and the text descriptions are often vague and ambiguous. How would you best address the challenge of aligning and fusing these two modalities to improve model performance?
Answer: C
Explanation:
DTW helps align time-series data with varying lengths and temporal distortions to text. Cross-modal attention then effectively fuses the aligned modalities, allowing the model to learn relationships between them. Resampling and direct concatenation (A) doesn't account for temporal variations. Ignoring data (B) is counterproductive. Averaging (D) loses temporal information. Averaging separate model outputs (E) is a form of late fusion and less effective than joint learning after alignment.
NEW QUESTION # 14
You are training a variational autoencoder (VAE) for generating images. The generated images are blurry and lack fine details. Which of the following modifications to the VAE architecture or training procedure would be MOST effective in improving the image quality?
Answer: A
Explanation:
Decreasing the KL divergence weight allows the model to focus more on reconstructing the input images and less on forcing the latent space to conform to a standard normal distribution. This can lead to sharper, more detailed images. Increasing the capacity of the networks or the dimensionality of the latent space may help, but only if the KL divergence weight is appropriately adjusted. Larger or smaller batch sizes do not directly influence the blurriness problem.
NEW QUESTION # 15
You are deploying a Riva-based speech-to-text service in a production environment. You observe high latency and CPU utilization on your server Which of the following actions would be most effective in optimizing the performance of your Riva service?
Answer: E
Explanation:
Enabling batching and concurrency is a key optimization strategy for Riva. It allows the server to process multiple audio streams simultaneously, maximizing GPU utilization and reducing overall latency. Switching to a smaller model (A) might reduce load but also decreases accuracy. Disabling punctuation (C) has a minor impact. Increasing audio chunk size (D) can help, but batching is more significant. Deploying on CPU (E) negates the benefits of Riva's GPU acceleration.
NEW QUESTION # 16
You are working with a multimodal generative model that combines text and image inputs. The model's performance is suboptimal when generating images conditioned on complex text descriptions. Which data analysis technique would be MOST effective in identifying the root cause of this issue?
Answer: C
Explanation:
Analyzing the correlation between text complexity and image quality directly investigates whether the model struggles with more intricate text descriptions. Sentiment analysis is relevant for identifying biases, but doesn't address the complexity issue directly. The average image resolution and pixel intensity are general image statistics and not specific to the multimodal problem. Object frequency in images can be useful but less direct than correlating text complexity with image quality.
NEW QUESTION # 17
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