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NVIDIA Generative AI Multimodal Sample Questions:

1. You're building a text generation model using a Transformer architecture. You observe that the generated text often gets stuck in repetitive loops, producing the same phrase over and over. Which of the following strategies is MOST likely to mitigate this issue?

A) Increase the temperature parameter during text generation.
B) Implement beam search with a larger beam width.
C) Increase the number of attention heads in the Transformer.
D) Decrease the learning rate of the model during training.
E) Use a smaller vccabulary size.

2. You are evaluating a multimodal model that generates descriptions for video clips. You have human ratings for the relevance, fluency, and coherence of the generated descriptions. Which statistical test is MOST appropriate for determining if there is a statistically significant difference in the median ratings for each of these criteria (relevance, fluency, coherence) between two different versions of your model?

A) Friedman Test
B) Kruskal-Wallis test
C) ANOVA
D) T-test
E) Mann-Whitney U test

3. You are building a multimodal emotion recognition system that uses facial expressions (images) and speech (audio). You want to use transfer learning to leverage pre-trained models for both modalities. You have access to a large pre-trained facial recognition model (trained on millions of faces) and a large pre-trained speech recognition model (trained on thousands of hours of speech). How do you design a multimodal transfer learning strategy to efficiently train the entire system on a smaller dataset of peoples face and audio samples?

A) Fine-tune each of the pre-trained models for the emotion recognition task using a joint loss function that combines the outputs of face emotion and speech emotion to create an overall expression.
B) Extract features separately using each of the pre-trained face and speech models and then train a separate classifier model, combining those features to recognize emotion.
C) Train the face model first, then train the audio model to recognize emotions based on the results of the facial expression emotions.
D) Use the features of the face data as an attention mechanism to pay attention to the audio, in an end-to-end learning model.
E) Train the Audio model first, then train the Face model to recognize emotions based on the results of the audio expression emotions.

4. You are fine-tuning a pre-trained Generative A1 model for a specific downstream task with limited dat a. The model starts to exhibit catastrophic forgetting of the original pre-trained knowledge. Which technique could mitigate this issue effectively?

A) Adding more data augmentation
B) Elastic Weight Consolidation (EWC)
C) Using a smaller batch size
D) Increasing the learning rate significantly
E) Freezing the weights of earlier layers in the network

5. You are evaluating two different generative A1 model architectures (Model A and Model B) for image generation. You use the Frechet Inception Distance (FID) score as your primary evaluation metric. Model A has a lower FID score than Model B. Which of the following statements are MOST accurate regarding the interpretation of the FID scores? (Select TWO)

A) Model A generates images that are more visually appealing to human observers.
B) Model A generates images that have a distribution more similar to the real image distribution used for calculating the FID score.
C) Model B generates images that are more diverse than Model A.
D) Model A is less likely to suffer from mode collapse than Model B.
E) Model B necessarily has better performance on downstream tasks using the generated images.

Solutions: