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NVIDIA Generative AI Multimodal Sample Questions:

1. You are building a generative AI model that creates realistic product designs based on textual descriptions and a reference image depicting a similar, but not identical, product. You are using a Variational Autoencoder (VAE) architecture. However, the generated images lack the fine-grained details present in the reference image. Which of the following methods would be most suitable to incorporate fine-grained details from the reference image into the generated design?

A) Replace the VAE with a Generative Adversarial Network (GAN).
B) Implement a skip connection from the encoder of the reference image to the decoder of the generative model, allowing the decoder to directly access features from the reference image at multiple scales.
C) Increase the latent space dimensionality of the VAE.
D) Reduce the batch size during training.
E) Use a larger convolutional kernel Size in the decoder

2. You're developing a system that analyzes video footage and generates textual summaries of the events occurring in the video. Which of the following architectures would be the MOST appropriate starting point for this task?

A) A transformer-based encoder-decoder architecture, with the encoder processing video frames (e.g., using a 3D CNN) and the decoder generating text.
B) A long short-term memory (LSTM) network for time series forecasting.
C) A generative adversarial network (GAN) for image generation.
D) A support vector machine (SVM) for classification.
E) A purely convolutional neural network (CNN) for image classification.

3. You are fine-tuning a pre-trained language model for a specific task. You notice that the model performs well on the training data but poorly on the validation dat a. Which of the following techniques can help mitigate this overfitting problem? (Select TWO)

A) Apply weight decay (L2 regularization).
B) Decrease the batch size.
C) Increase the learning rate.
D) Increase the size of the training data.
E) Use dropout regularization.

4. You are tasked with building a system that can answer questions based on both an image and a corresponding text description. The image is represented as a feature vector from a CNN, and the text is represented as a sequence of word embeddings from a pre-trained language model. Which architecture would be most suitable for this task?

A) A combination of a CNN and an LSTM, where the CNN processes the image and the LSTM processes the text independently.
B) A simple feedforward neural network that concatenates the image and text feature vectors.
C) A Transformer-based architecture with cross-attention mechanisms that allow the model to attend to both the image and text features simultaneously.
D) A recurrent neural network (RNN) that processes the text and then uses the final hidden state to attend to the image features.
E) Two separate models, one for processing images and another for processing text, with the final answer being chosen based on the higher confidence score.

5. You're building a virtual assistant using NVIDIAAvatar Cloud Engine (ACE). You want the avatar to respond to user queries with realistic facial expressions and lip synchronization. Which ACE components are essential for achieving this?

A) Riva ASR, Riva TTS, and Audi02Emotion.
B) only Riva ASR and TTS.
C) Only a 3D avatar model.
D) Riva ASR, Riva TTS, Audi02Emotion, and a 3D avatar model.
E) Riva ASR, Riva TTS, Audi02Emotion, a 3D avatar model, and an animation engine.

Solutions: