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NVIDIA Generative AI Multimodal Sample Questions:

1. You're building an application utilizing NVIDIA ACE to create interactive virtual assistants. The goal is to have the assistant respond to user queries in a natural and contextually relevant way. Which of the following choices, when implemented together, would significantly contribute to achieving this objective?

A) Using a small, low-latency speech recognition model to ensure quick response times, even if it results in reduced accuracy and frequent errors.
B) Employing Riva for accurate Automatic Speech Recognition (ASR) and Text-to-Speech (TTS), utilizing a large language model (LLM) fine-tuned on a domain-specific dataset for natural language understanding and response generation, and integrating a dialogue management system to maintain conversation context and manage turn-taking.
C) Relying solely on pre-trained LLMs without fine-tuning for the specific application domain, assuming that the models will generalize well to all types of user queries.
D) Primarily focusing on high-fidelity avatar rendering using Omniverse, neglecting the accuracy of speech recognition and the quality of the generated responses.
E) Using a rule-based system for response generation, avoiding the complexity of training and deploying an LLM.

2. You are training a deep convolutional generative adversarial network (DCGAN) for generating high-resolution images. After several epochs, you observe mode collapse the generator produces only a few similar images. Which of the following strategies would be most effective in mitigating mode collapse?

A) Decrease the learning rate of the generator and discriminator simultaneously.
B) Use label smoothing in the discriminator to penalize overconfident predictions.
C) Increase the batch size significantly to provide the discriminator with a more diverse set of samples.
D) Implement feature matching in the discriminator by making the generator learn to match intermediate layer activations of the discriminator on real data.
E) Introduce batch normalization only in the generator network.

3. You are tasked with building a system that can generate captions for images. You want to use a transformer-based model. During inference, you notice that the model tends to generate repetitive captions. Which of the following decoding strategies could you use to mitigate this issue?

A) Top-k sampling
B) Beam search with a high beam width
C) Greedy decoding
D) Beam search with a length penalty
E) Random sampling

4. Consider a multimodal dataset containing patient records: text descriptions of symptoms, MRI images, and audio recordings of heart sounds. Some records are missing MRI images. Which of the following methods is BEST suited for handling this missing data within a multimodal learning framework?

A) Deleting all records with missing MRI images.
B) Using a masking approach during training, where the model is trained to predict the missing modality (MRI) from the available modalities (text and audio) for incomplete records and is trained with all modalities for complete records.
C) Ignoring the MRI data completely and training the model only on the text and audio data.
D) Imputing missing MRI images using the average MRI image from the entire dataset.
E) Training a separate model only on records with complete data and then using it to predict the missing data.

5. You observe that the generated images often lack fine-grained details and tend to be blurry. Which of the following techniques could MOST effectively improve the visual quality of the generated images?

A) Using a larger dataset of text-image pairs.
B) Decreasing the learning rate during training.
C) Increasing the batch size during training.
D) Using a variational autoencoder (VAE) instead of a GAN.unlikely to significantly improve diagnosis accuracy.
E) Implementing a discriminator network and using adversarial training (GAN).

Solutions: