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NVIDIA Generative AI Multimodal Sample Questions:

1. You are developing a system to automatically generate image descriptions for visually impaired users. The system uses a combination of object detection, attribute recognition, and relationship extraction. However, the generated descriptions often lack detail and fail to capture the nuances of the image content. Which of the following strategies would MOST effectively address this limitation?

A) Use a more powerful transformer-based model (e.g., GPT-3) to generate the image descriptions from the extracted object, attribute, and relationship information.
B) Combine B and C.
C) Manually rewrite a subset of descriptions to be more in line with the requirements.
D) Incorporate visual attention mechanisms that allow the description generation model to focus on the most salient regions of the image.
E) Increase the size of the training dataset for the object detection model.

2. You are tasked with visualizing the performance of a Generative A1 model across different categories of input dat a. You need to show both the accuracy and the number of data points in each category. Which visualization technique would be MOST effective for this purpose?

A) A bar chart showing the accuracy for each category, with error bars indicating the sample size.
B) A table showing the accuracy and sample size for each category.
C) A scatter plot showing the relationship between accuracy and sample size for each category.
D) A combination chart (e.g., bar and line) with bars showing the accuracy and a line showing the sample size.
E) A pie chart showing the accuracy for each category.

3. Which data augmentation techniques are MOST suitable for improving the robustness of a multimodal model that uses images and text?

A) Adding Gaussian noise to images and randomly deleting words in the text.
B) Changing the image resolution and increasing the text size.
C) Rotating images and back-translating text (translating to another language and back to the original).
D) Randomly cropping images and translating text to different languages.
E) Rescaling images and changing the font of the text.

4. 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)

A) The image features are noisy or of poor quality, confusing the model.
B) The image features are irrelevant to the sentiment expressed in the text.
C) The text encoder is too complex, hindering the model's ability to process image information.
D) The model is overfitting to the image features.
E) The model is not properly aligning the text and image features, leading to conflicting signals.

5. You are building a multimodal AI system that generates 3D models of furniture from text descriptions and a few 2D images of similar furniture pieces. The system uses separate encoders for text and images. You want to fuse the information from both modalities effectively. Which TWO of the following fusion techniques would be the most appropriate for this task, considering the different nature of the text and image data?

A) Using a gating mechanism (e.g., a learned weight) to control the contribution of the text and image embeddings based on the input.
B) Performing a simple average of the text and image embeddings.
C) Using a cross-attention mechanism where the text embedding attends to the image features, and vice-versa, allowing the model to dynamically weight the importance of different parts of each modality.
D) Applying element-wise addition of the text and image embeddings.
E) Simple concatenation of the text and image embeddings before feeding them into the decoder.

Solutions: