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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Experimentation | 25% | - Model evaluation and comparison - Hypothesis testing - A/B testing - Experimental design |
| Topic 2: Trustworthy AI | 5% | - Ensuring fairness and transparency - Ethical considerations in AI development |
| Topic 3: Multimodal Data | 15% | - Handling and integrating text, image, and audio data - Applications and use cases |
| Topic 4: Core ML & AI Knowledge | 20% | - Basic concepts and terminology - Key algorithms and techniques |
| Topic 5: Performance Optimization | 10% | - Monitoring and improving system efficiency - Techniques for optimizing AI performance |
| Topic 6: Data Analysis & Visualization | 10% | - Data preprocessing and feature engineering - Visualization techniques for multimodal data |
| Topic 7: Software Development & Engineering | 15% | - Integration and deployment of multimodal AI systems - Python libraries for multimodal AI |
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NEW QUESTION # 21
You're working on a project involving multimodal transfer learning for generating recipes from images of dishes and ingredient lists. You have a large dataset of images but a limited dataset of paired images and ingredient lists. You decide to leverage a pre-trained image model and a pre-trained text model. However, you are facing catastrophic forgetting after fine-tuning the models on the paired image and ingredient list dat a. Which of the following techniques would be MOST effective in mitigating catastrophic forgetting while adapting the pre-trained models to the new task?
Answer: E
Explanation:
Using adapter modules is a common technique to mitigate catastrophic forgetting. By freezing most of the pre-trained weights and only training a small adapter, you preserve the knowledge learned during pre-training while adapting the model to the new task. Training from scratch would negate the benefits of transfer learning. A high learning rate can exacerbate forgetting. L1 regularization can prevent overfitting but doesn't directly address forgetting. Increasing batch size might improve generalization but doesn't solve the core issue of catastrophic forgetting.
NEW QUESTION # 22
You're building a multimodal sentiment analysis model using text and audio dat a. You observe that the model's performance is significantly worse on audio samples from noisy environments. Which of the following data augmentation techniques would be MOST effective for improving the model's robustness to noisy audio?
Answer: E
Explanation:
Mixing different types of noise at varying SNRs is the most effective data augmentation technique for improving robustness to noisy audio because it directly simulates the real-world noise conditions that the model will encounter during deployment This helps the model learn to extract meaningful features from noisy audio. Adding noise to the text data is not relevant to the audio noise issue_ Time stretching and pitch shifting are useful for augmenting audio data, but they do not directly address the noise problem. Random cropping is primarily for image data.
NEW QUESTION # 23
You are working on a project that involves generating high-resolution images using a StyleGAN architecture. You observe that while the generated images are generally realistic, they often exhibit 'water droplet' artifacts. What could be a cause and solution to these artifacts?
Answer: E
Explanation:
Water droplet artifacts often arise from aliasing in the upsampling layers of the generator network. Filtered upsampling (e.g., using a blur kernel before upsampling) or explicit anti-aliasing layers can smooth out the image and reduce these artifacts. Instabilities during adversarial training may amplify the artifacts. Gradient penalty helps stabilize the discriminator, thus improving the overall training stability and results.
NEW QUESTION # 24
You are analyzing a dataset of customer reviews for a new product using Natural Language Processing (NLP). The dataset contains both positive and negative reviews, but a significant portion of the negative reviews uses sarcasm. Which of the following NLP techniques would be MOST effective in accurately identifying the sentiment expressed in sarcastic reviews?
Answer: E
Explanation:
Pre-trained transformer models capture contextual information and complex relationships between words, which is crucial for detecting sarcasm. Bag-of-words, sentiment lexicons, and rule-based systems rely on individual words or simple patterns and are easily fooled by sarcasm. Averaging word embeddings loses the contextual information vital for sarcasm detection.
NEW QUESTION # 25
You're building a real-time voice cloning application using NVIDIA Riv
a. You need to ensure high-quality synthesized speech with minimal latency. Which of the following Riva configurations would provide the BEST trade-off between quality and speed?
Answer: D
Explanation:
FastSpeech and parallel WaveGAN offer a good balance of speed and quality. Multi-GPU deployment and TensorRT optimization further reduce latency. Tacotron 2 and WaveGlow (A) provide higher quality but are slower. CPU-based vocoders (C) are generally too slow for real-time applications. TPIJs (D) are not directly supported by Riva. (E) does not leverage Riva features. Riva's TensorRT optimizatins would be ideal here. The best option would be a faster TTS model along with a Vocoder for real time synthesis.
NEW QUESTION # 26
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