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NVIDIA Generative AI Multimodal Sample Questions (Q255-Q260):
NEW QUESTION # 255
Consider a multimodal dataset containing text, images, and corresponding GPS coordinates. You want to build a model that predicts the sentiment of a social media post based on this dat a. Which of the following data preprocessing steps are crucial to ensure the model's performance and prevent data leakage?
- A. Resize all images to a uniform size.
- B. Split the dataset into training, validation, and test sets based on time to avoid leakage of future information into the training set.
- C. Standardize the GPS coordinates (latitude and longitude) using a scaler fitted only on the training data.
- D. Randomly shuffle the entire dataset before splitting it into training, validation, and test sets.
- E. Normalize all text data to lowercase and remove punctuation.
Answer: A,B,C,E
Explanation:
Normalizing text (A) and resizing images (B) are standard preprocessing steps. Time-based splitting (C) prevents data leakage by ensuring that the model is not trained on future data. Standardizing GPS coordinates (E) with training data prevents the test data from influencing the scaling. Random shuffling before splitting (D) can lead to data leakage in time-series data.
NEW QUESTION # 256
Consider the following Python code snippet using PyTorch Lightning and a Hugging Face Transformers model for multimodal classification. Which of the following code snippets is MOST appropriate to perform gradient accumulation in this context, assuming you want to accumulate gradients over 4 batches?
- A.

- B.

- C.

- D.

Answer: D
Explanation:
PyTorch Lightning provides a built-in argument in the 'Trainer' class to easily enable gradient accumulation. Setting will accumulate gradients over 4 batches before performing an optimizer step.
NEW QUESTION # 257
Which of the following statements are TRUE regarding the challenges of training multimodal machine learning models? (Select TWO)
- A. All available open-source tools readily support multimodal architectures and loss functions, so there are no software-related challenges.
- B. Handling missing modality data (e.g., missing image for a text input) requires specialized techniques.
- C. Multimodal models are immune to the problem of overfitting due to the diverse nature of the input data.
- D. Aligning data from different modalities with varying temporal resolutions (e.g., high-frame-rate video and low-frequency audio) is a significant challenge.
- E. Multimodal models are generally easier to train than unimodal models due to the increased information available.
Answer: B,D
Explanation:
Multimodal models are harder to train than unimodal ones. Missing modality data requires specific handling. Different temporal resolutions need alignment. Overfitting remains a concern. Open source availability is improving, but multimodal specific support remains more niche than unimodal.
NEW QUESTION # 258
Which of the following are key benefits of using multimodal learning compared to unimodal learning? (Select TWO correct answers)
- A. Simpler model architectures.
- B. Reduced computational complexity.
- C. Improved robustness to noise and missing data in one modality.
- D. Guaranteed perfect accuracy.
- E. Enhanced ability to capture complex relationships between different data types.
Answer: C,E
Explanation:
Multimodal learning leverages information from multiple modalities, which can lead to improved robustness because the model can rely on other modalities when one is noisy or incomplete. It also allows the model to learn more complex relationships that might not be apparent from a single modality.
NEW QUESTION # 259
You are working on a project to generate realistic images from text descriptions. You've trained a diffusion model, but the generated images often lack fine-grained details and exhibit artifacts. Which of the following techniques would be MOST effective in improving the image quality and fidelity?
- A. Increase the number of diffusion steps during image generation.
- B. Use a larger batch size during training.
- C. Reduce the learning rate during training.
- D. Implement classifier-free guidance, adjusting the guidance scale to balance fidelity and diversity.
- E. None of the above.
Answer: D
Explanation:
Classifier-free guidance allows you to control the influence of the text description on the generated image. By adjusting the guidance scale, you can find a balance between generating images that are faithful to the text description and generating diverse and high-quality images. Increasing diffusion steps, batch size, or reducing the learning rate may help but is less targeted towards improving fidelity specifically.
NEW QUESTION # 260
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