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NVIDIA Generative AI Multimodal Sample Questions (Q191-Q196):

NEW QUESTION # 191
You are developing a generative A1 model to create music based on textual descriptions of mood and genre. You have a dataset of paired text descriptions and music tracks. When evaluating the generated music, you realize it's difficult to objectively quantify the quality of the music. Which of the following evaluation methods would provide the MOST comprehensive assessment of the generated music's quality and alignment with the text descriptions?

A. Use a pre-trained music genre classification model to predict the genre of the generated music and compare it to the genre in the text description.
B. Calculate the Bit Rate of the generated music tracks.
C. Calculate the Root Mean Square (RMS) energy of the generated music tracks.
D. Conduct a human evaluation study where participants rate the generated music on various subjective criteria such as pleasantness, mood alignment, and overall quality based on the text description provided.
E. Measure the file size of the generated music tracks.

Answer: D

Explanation:
Human evaluation (C) is crucial for subjective aspects like music quality. Bit rate (A), file size (D), and RMS energy (E) are not related to music quality directly. While genre classification (B) can be a component, it doesn't capture the full scope of quality or mood alignment.

NEW QUESTION # 192
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?

A. Increasing the learning rate during training.
B. Implementing a hierarchical attention mechanism that first attends to relevant time steps and then to relevant regions within those time steps.
C. Adding more layers to the LSTM or GRIJ used for sequence modeling.
D. Using a larger batch size.
E. Decreasing the regularization strength.

Answer: B

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 # 193
Consider the following Python code snippet using PyTorch, designed to combine text and image embeddings before feeding them into a transformer. Assume 'text_embedding' has shape '(batch_size, seq_len, hidden_dim)' and 'image_embedding' has shape '(batch_size, image_features)'. Which of the following code snippets MOST correctly combines these embeddings for a multimodal transformer input?

Answer: C

Explanation:
Option B correctly expands the image embedding to match the sequence length of the text embedding and then concatenates them along the hidden dimension (dim=2), creating a combined multimodal input. Option A is incorrect as the shapes are incompatible, and Option C performs element-wise addition, requiring compatible shapes. Option D performs matrix multiplication, which is not a suitable way to combine embeddings. Option E is not a proper way of combining the tensors

NEW QUESTION # 194
You are working on a project that involves generating realistic images of furniture based on textual descriptions. The input data consists of text descriptions and a small dataset of existing furniture images. Which data augmentation techniques would be MOST effective in improving the quality and diversity of the generated images?

A. Randomly cropping and rotating the existing furniture images.
B. Combining A, B, and C.
C. Synthesizing new text descriptions using paraphrasing and back-translation techniques.
D. Using generative adversarial networks (GANs) to generate new furniture images from the existing dataset.
E. Focusing solely on increasing the size of the text dataset and ignoring image augmentation.

Answer: B

Explanation:
Combining all techniques provides the best results. Image augmentations like cropping and rotation increase the variance of the image data. GANs create entirely new images, and text augmentation enhances the diversity of the input descriptions. Focusing only on one modality will likely limit the model's performance.

NEW QUESTION # 195
You're working with a multimodal model that fuses text and image features. You've noticed that the model performs poorly when the text and image are semantically misaligned (e.g., an image of a dog and the caption 'a cat on a mat'). Which of the following techniques can help improve the model's robustness to such misalignment?

A. Adding a contrastive loss that penalizes embeddings of misaligned text-image pairs.
B. Increasing the learning rate during training.
C. Using only positive text-image pairs for training.
D. Decreasing the batch size.
E. Removing dropout layers from the model architecture.

Answer: A

Explanation:
A contrastive loss function directly addresses the issue of semantic misalignment by penalizing the model when it produces similar embeddings for text and images that don't correspond semantically. This encourages the model to learn more robust and meaningful feature representations.

NEW QUESTION # 196
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