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NVIDIA Generative AI Multimodal Sample Questions:

1. You have trained a multimodal model to generate descriptions for recipes, using images of the finished dish as one modality and a list of ingredients as another. When evaluating the generated descriptions, you notice the descriptions are factually correct in terms of ingredients but often fail to capture the stylistic nuances or tone of professionally written recipes. Which evaluation strategy would provide the most insightful feedback on this aspect of the model's performance?

A) Conduct a human evaluation where individuals rate the generated descriptions based on factors such as informativeness, clarity, and stylistic appropriateness.
B) Measure the perplexity of the generated descriptions using a language model trained on a corpus of recipe books.
C) Use a pre-trained language model to assess the fluency and coherence of the generated descriptions.
D) Compute the ROUGE score between the generated descriptions and a set of reference recipes.
E) Calculate the BLEU score between the generated descriptions and a set of reference recipes.

2. You're using a pre-trained multimodal model that combines visual and textual information for a new downstream task: generating marketing slogans for product images. The model performs poorly, generating generic slogans that are unrelated to the specific product features. What is the MOST effective strategy to adapt this pre-trained model to your specific task?

A) Freeze the pre-trained model's weights and train a separate model to map the pre-trained model's output to marketing slogans.
B) Fine-tune the entire pre-trained model on a dataset of product images and corresponding marketing slogans.
C) Only fine-tune the visual encoder component of the pre-trained model.
D) Use the pre-trained model as is, without any adaptation.
E) Replace the model's output layer with a new layer trained specifically to generate marketing slogans.

3. Consider this PyTorch code snippet related to processing multimodal dat a. What is the primary purpose of the following code in the context of Generative A1?

A) To resize all images to the same dimension.
B) To create separate data loaders for images and text.
C) To ensure images and text are processed in the same order during training.
D) To concatenate image and text data into a single tensor.
E) To create a custom dataset class for handling paired image and text data.

4. When deploying a multimodal Generative A1 model for a real-time application, such as a virtual assistant that responds to voice commands and displays relevant images, which of the following considerations are MOST critical for ensuring low latency and a smooth user experience? (Select TWO)

A) Prioritize model accuracy over inference speed.
B) Utilize asynchronous processing and caching strategies to pre-compute and store frequently accessed data.
C) Employ model quantization and pruning techniques to reduce model size and computational requirements.
D) Disable any logging or monitoring to reduce overhead.
E) Deploy the model on a single CPU core to minimize resource contention.

5. You are building a Generative A1 application that processes images and text. The image data has missing pixel values, and the text data contains inconsistencies in abbreviations. Which data preprocessing techniques are MOST suitable to address these issues effectively?

A) Image: Replacing missing pixels with zero; Text: Ignoring abbreviations during analysis.
B) Image: KNN imputation for missing pixels; Text: Applying regular expressions to expand abbreviations.
C) Image: Median imputation for missing pixels; Text: Using a fuzzy matching algorithm to correct inconsistencies in abbreviations.
D) Image: Mean imputation for missing pixels; Text: Standardizing abbreviations using a predefined mapping.
E) Image: Deleting rows with missing pixel values; Text: Removing all abbreviations from the text data.

Solutions: