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| Section | Weight | Objectives |
|---|---|---|
| Trustworthy AI | 5% | - Robustness and error mitigation - Ethical considerations and responsible use - Reliability, fairness, and safety in generative systems |
| Experimentation | 25% | - Experiment design and methodology - Metrics and validation strategies for generative models - Model training, fine-tuning, and evaluation |
| Performance Optimization | 10% | - Scalability and deployment considerations - Hardware acceleration with NVIDIA platforms - Model efficiency and inference optimization |
| 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 |
| Software Development and Engineering | 15% | - Development workflows for generative AI applications - Libraries, frameworks, and tools for multimodal AI - Best practices for building and maintaining systems |
| Data Analysis and Visualization | 10% | - Visualization techniques for model behavior and results - Analyzing multimodal datasets and outputs - Interpretation of generative AI outputs |
| Multimodal Data | 15% | - Multimodal model architectures and integration - Characteristics of text, image, and audio data - Data preprocessing, fusion, and representation |
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Fast2test provides you with NVIDIA NCA-GENM exam questions in 3 different formats to open up your study options and suit your preparation tempo. The NVIDIA NCA-GENM PDF is the most convenient format to go through all exam questions easily. It is a compilation of actual NVIDIA NCA-GENM exam questions and answers.
NEW QUESTION # 28
Which of the following regularization techniques is MOST effective for preventing overfitting in a multimodal deep learning model with a large number of parameters and complex interactions between different modalities?
Answer: E
Explanation:
Dropout is particularly effective for preventing overfitting in deep neural networks with many parameters. It randomly drops out neurons during training, forcing the network to learn more robust features and reducing co-adaptation between neurons. L1 and L2 regularization can also help, but they might not be as effective as dropout for very deep and complex models. Batch Normalization primarily helps with training stability and speed, although it can also have a slight regularization effect. Early stopping prevents overfitting by stopping training when the performance on a validation set starts to degrade.
NEW QUESTION # 29
You're training a model to generate code snippets from natural language descriptions. You are using a Transformer architecture and a large dataset of code examples. You notice the model frequently generates syntactically correct code, but the code doesn't accurately implement the described functionality (i.e., it's semantically incorrect). Select TWO methods which could improve the semantic correctness of the generated code.
Answer: C,E
Explanation:
Fine-tuning with unit tests (B) and using reinforcement learning with unit test rewards (D) directly address semantic correctness- The model learns to generate code that not only compiles but also behaves as expected- Increasing Transformer layers (A) or vocabulary size (E) might improve syntactic correctness or fluency, but don't guarantee semantic accuracy Beam search (C) generates more diverse outputs, but doesn't explicitly guide the model towards semantic correctness.
NEW QUESTION # 30
You are working on a multimodal model for video captioning, where the model needs to generate captions describing the actions and events happening in a video. You notice that the model tends to focus only on the most salient objects in the scene and ignores subtle but important actions. Which of the following techniques can help the model attend to these subtle actions and generate more comprehensive captions?
Answer: E
Explanation:
A hierarchical attention mechanism is the MOST appropriate technique. By first attending to relevant time steps (which might contain the subtle actions) and then attending to relevant regions within those time steps, the model can focus on the specific parts of the video that are most informative for describing the subtle actions. Increasing learning rate (A), using a larger batch size (B), adding more layers (D), and decreasing regularization strength (E) are unlikely to solve the problem of attending to subtle actions specifically. These can all improve performance, but they don't address the attention mechanism itself.
NEW QUESTION # 31
A multimodal AI model, designed to translate sign language videos into text, is consistently failing on specific gestures that involve rapid hand movements. Which optimization technique targeting temporal data processing would be MOST effective to apply?
Answer: C
Explanation:
RNNs and Transformers, especially those with attention, are designed to capture temporal dependencies in sequential data like video. Increasing frame rate (B) might help slightly, but doesn't address the fundamental issue of modeling sequential data effectively. The other options are either irrelevant or detrimental to capturing the rapid hand movements. RNNs/Transformers capture the movement itself, which is key here. Image segmentation could be a preprocessing step, but RNN/Transformer is the core optimization.
NEW QUESTION # 32
When experimenting with different architectures for a text-to-image model, you observe that a Diffusion model generates higher quality images than a GAN (Generative Adversarial Network). However, the Diffusion model is significantly slower to generate images. What strategy can you employ to improve the inference speed of the Diffusion model without significantly sacrificing image quality?
Answer: C
Explanation:
Model distillation involves training a smaller, faster 'student' model to mimic the behavior of a larger, slower 'teacher' model. This allows you to retain much of the quality of the original model while significantly improving inference speed. Increasing the number of diffusion steps or using a larger UNet would further slow down the Diffusion model. Training the GAN longer doesn't address the speed issue of the Diffusion model. Using a smaller batch size might help with memory limitations, but won't significantly improve inference speed.
NEW QUESTION # 33
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