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| Section | Weight | Objectives |
|---|---|---|
| Trustworthy AI | 5% | - Reliability, fairness, and safety in generative systems - Ethical considerations and responsible use - Robustness and error mitigation |
| Software Development and Engineering | 15% | - Best practices for building and maintaining systems - Libraries, frameworks, and tools for multimodal AI - Development workflows for generative AI applications |
| Data Analysis and Visualization | 10% | - Visualization techniques for model behavior and results - Analyzing multimodal datasets and outputs - Interpretation of generative AI outputs |
| Performance Optimization | 10% | - Hardware acceleration with NVIDIA platforms - Model efficiency and inference optimization - Scalability and deployment considerations |
| 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 |
| Experimentation | 25% | - Model training, fine-tuning, and evaluation - Experiment design and methodology - Metrics and validation strategies for generative models |
| Multimodal Data | 15% | - Multimodal model architectures and integration - Data preprocessing, fusion, and representation - Characteristics of text, image, and audio data |
>> NCA-GENM New Practice Questions <<
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NEW QUESTION # 39
You are developing a multimodal sentiment analysis model that combines text reviews and product images. You observe that the model's performance is significantly better when only text is used, compared to when both text and images are combined. What are the potential reasons for this performance degradation, and how can you address them effectively? (Choose two)
Answer: A,D
Explanation:
Poor quality or noisy image features can confuse the model. Incorrect alignment between text and image features leads to conflicting signals, hindering the model's ability to learn a cohesive representation. Irrelevant image features, overfitting and text encoder complexity are all potential reasons that are less likely than the chosen answers.
NEW QUESTION # 40
Consider a scenario where you are evaluating the performance of a multimodal A1 model that generates descriptions for images. However, the generated descriptions tend to be repetitive and lack diversity. Which of the following techniques can be employed to address this issue and encourage more diverse and creative outputs from the model? (Select TWO)
Answer: B,E
Explanation:
Nucleus sampling (top-p sampling) randomly samples from the smallest set of words whose cumulative probability mass exceeds a threshold p, encouraging more diverse outputs. Increasing the temperature scaling parameter makes the probability distribution flatter, leading to more exploration and less predictable (more creative) outputs. Beam search with a small beam width might still result in repetitive outputs. Increasing the training data size can help, but it might not directly address the lack of diversity. Greedy decoding always selects the most probable word, leading to repetitive and predictable outputs.
NEW QUESTION # 41
You're using Stable Diffusion with a custom prompt to generate images of landscapes. You notice that the generated images consistently lack detail and appear blurry, despite increasing the number of inference steps. Which of the following prompt engineering techniques, combined with appropriate parameter tuning, is MOST likely to address this issue and improve the image's sharpness and detail?
Answer: B
Explanation:
Adding keywords specifically related to image quality ('photorealistic', 'high resolution', '8k', 'detailed') helps guide the model towards generating sharper and more detailed images. 'clip_skip' influences the model to incorporate more details into the output images. Adjusting 'clip_skip' along with quality prompt keywords will enhance the image quality.
NEW QUESTION # 42
Consider a multimodal generative A1 model that produces images based on textual prompts. The model is prone to generating images that are similar to those in the training data, resulting in a lack of novelty. Which hyperparameter adjustment would be MOST effective in increasing the diversity of the generated images?
Answer: A
Explanation:
Increasing the temperature parameter during decoding introduces more randomness into the sampling process, leading to a wider range of possible outputs and thus increasing the diversity of generated images. Decreasing batch size doesn't directly affect diversity. Reducing the learning rate affects training, not the diversity of generated images after training. Decreasing layers might reduce model capacity but not necessarily increase diversity. Increasing weight decay can prevent overfitting but doesn't directly address the lack of novelty in generated images.
NEW QUESTION # 43
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: A
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 # 44
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