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NVIDIA Generative AI Multimodal Sample Questions:

1. You are experimenting with different loss functions for training a Variational Autoencoder (VAE) to generate images. You observe that using only the reconstruction loss (e.g., Mean Squared Error) results in blurry images. What other loss component is typically added to the VAE objective function to encourage the latent space to be well-structured and generate sharper images?

A) Contrastive loss
B) Cross-entropy loss
C) Hinge loss
D) Perceptual loss
E) Kullback-Leibler (KL) divergence loss

2. You are developing a virtual assistant using NVIDIAACE. You want to ensure that the avatar's facial expressions and lip movements are synchronized with the generated speech in real-time. Which NVIDIA SDKs and ACE components are essential for achieving this?

A) NeMo for text-to-speech, Audio2Face for generating blendshape weights, and a real-time rendering engine (e.g., Unity or Unreal Engine) to drive the avatar.
B) Riva for speech synthesis, NeMo for language modeling, and Audi02Face for animation.
C) Riva for speech recognition, Triton Inference Server for model deployment, and Omniverse for 3D rendering.
D) CUDA for GPU acceleration, TensorRT for model optimization, and Audi02Emotion for expression generation.
E) Triton Inference Server for deploying all AI models, Riva for voice cloning, and Omniverse for character creation.

3. Consider the following Python code snippet using PyTorch, intended for fusing image and text features in a multimodal model. Assume 'image_featureS and 'text_features' are tensors of shape Which of the following fusion methods is implemented in this code?

A) Cross-modal Attention
B) Concatenation
C) Tensor Product
D) Element-wise Addition
E) Gated Attention

4. You are developing a multimodal model that combines time-series data from sensor readings with natural language descriptions of events. The time-series data has varying sampling rates and the text descriptions are often vague and ambiguous. How would you best address the challenge of aligning and fusing these two modalities to improve model performance?

A) Resample the time-series data to a uniform sampling rate and directly concatenate it with the text embeddings.
B) Train separate models for time-series and text and average their predictions.
C) Average the time-series data over a fixed time window and concatenate it with the text embeddings.
D) Use a dynamic time warping (DTW) algorithm to align the time-series data with the text descriptions and then use a cross-modal attention mechanism for fusion.
E) Ignore the time-series data and train the model only on the text descriptions.

5. You are training a multimodal Generative A1 model for generating video captions. The model is overfitting to the training data, resulting in poor generalization to unseen videos. Which of the following regularization techniques would be MOST suitable to mitigate overfitting?

A) Batch normalization
B) Early stopping based on a validation set
C) Data augmentation (e.g., adding noise to the video frames)
D) Dropout
E) L1 regularization on the model weights

Solutions: