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NVIDIA Generative AI Multimodal Sample Questions:

1. You are building a Generative A1 model that generates captions for images. You want to evaluate the quality of the generated captions.
Which evaluation metrics are MOST suitable for this task?

A) Cosine Similarity and Euclidean Distance.
B) BLEU, ROUGE, and CIDEr.
C) Accuracy and Precision.
D) Fl-score and AUC.
E) Mean Squared Error (MSE) and Root Mean Squared Error (RMSE).

2. Consider the following code snippet intended to generate an image embedding using CLIP. What is the most likely reason for the 'RuntimeErroN?

A) The image size is not compatible with the CLIP model's input requirements.
B) The CLIP model was not properly loaded onto the GPIJ.
C) The image is not in RGB format.
D) The image tensor does not require gradient calculation.
E) The image pixel values are not normalized correctly.

3. You're building a multimodal model that integrates text, images, and audio. The text data has many missing values. Which of the following strategies would be MOST effective for handling missing text data while leveraging the other modalities?

A) Use a multimodal generative model (e.g., VAE, GAN) to impute the missing text based on the learned joint representation of all modalities.
B) Use a simple imputation method like replacing missing text with a placeholder like 'unknown'.
C) Ignore the missing text values during training, assuming the model can learn from the available modalities.
D) Train a separate model to predict the missing text based on the available image and audio data, then impute the predicted values.
E) Remove all data points with missing text values to ensure data integrity.

4. Consider the following code snippet, where you are trying to load image and text data for a multimodal model. What is the most likely cause of error if the code fails during the image loading step?

A) The text data is not in IJTF-8 encoding.
B) The system doesn't have CUDA drivers installed.
C) The image files are corrupted or in an unsupported format.
D) The batch size is too large.
E) The learning rate is set too high.

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

Solutions: