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NVIDIA Generative AI Multimodal Sample Questions:

1. 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) Deploy the model on a single CPU core to minimize resource contention.
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) Prioritize model accuracy over inference speed.
E) Disable any logging or monitoring to reduce overhead.

2. You're training a conditional GAN to generate images of birds based on text descriptions. The GAN generates images, but they lack fine- grained details and often have artifacts. Which of the following techniques are MOST likely to improve the quality and realism of the generated images? (Select TWO)

A) Using a simple Multi-Layer Perceptron (MLP) as the generator.
B) Using a deeper and wider generator network (e.g., with more layers and channels).
C) Reducing the size of the input noise vector to the generator.
D) Using a more powerful discriminator architecture (e.g., with attention mechanisms).
E) Implementing spectral normalization in both the generator and discriminator.

3. You are experimenting with a text-to-image generative model. You notice that when prompted with descriptions containing specific demographic information (e.g., 'a black doctor'), the generated images consistently reflect stereotypes. What steps can you take during the experiment evaluation phase to identify and mitigate this bias? (Select TWO)

A) Conduct a human evaluation study where participants assess the generated images for stereotypical representations.
B) Randomly shuffle the training dataset to minimize bias.
C) Use a bias detection metric to quantify the presence of bias in the generated images, comparing output distributions across different demographic groups.
D) Increase the size of the training dataset to dilute the effect of any biased examples.
E) Filter out all examples containing demographic information from the training dataset.

4. You are working on a project that involves analyzing customer reviews which contains the following dataset: 1. customer_id(categorical) 2. customer_review(text) 3. product_image(image) 4. video_of_product_usage(video) What is the best way to handle and address the problem of skewness across each modailities?

A) Apply modality-specific weighting schemes that assign higher weights to modalities with less representation.
B) Do nothing about the skewness, as the model will learn to adapt to the imbalanced data distribution.
C) Balance the dataset by oversampling under-represented data points within each modality independently.
D) Design a loss function that explicitly penalizes the model for being biased towards dominant modalities.
E) Treat all modalitites with equal weights during model training, ignoring potential skewness issues.

5. You are building a multimodal emotion recognition system that takes both facial expressions (images) and speech audio as input. During development, you observe that the model is heavily biased towards the audio modality, effectively ignoring the visual input. Which technique would be the LEAST effective in mitigating this modality bias?

A) Modality dropout: Randomly dropping out one of the modalities during training.
B) Increasing the complexity of the audio processing branch and simplifying the image processing branch of the model.
C) Adversarial training to make each modality indistinguishable.
D) Reweighting the loss function to penalize errors made based on the less dominant modality (image).
E) Gradient blending: Adjusting the gradients from each modality based on their relative importance.

Solutions: