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NVIDIA Generative AI Multimodal Sample Questions (Q252-Q257):
NEW QUESTION # 252
You are developing a system to generate captions for videos. The video frames are processed using a pre-trained ResNet model, and the audio track is processed using a pre-trained Wav2Vec model. Which of the following techniques is MOST suitable for aligning the visual and audio features to generate accurate and coherent captions?
- A. Using cross-attention mechanisms where the audio features attend to the visual features, and vice-versa, before feeding them into a Transformer decoder.
- B. Training separate LSTMs for visual and audio features and averaging their outputs.
- C. Using a simple feedforward network to combine the ResNet and Wav2Vec features.
- D. Concatenating the ResNet and Wav2Vec features and feeding them into a single LSTM.
- E. Ignoring the audio track and only using the video frames.
Answer: A
Explanation:
Cross-attention allows the model to learn the temporal relationships and dependencies between the visual and audio modalities. The audio features can attend to relevant visual features at each time step, and vice versa, leading to better alignment and more coherent captions. Simple concatenation and averaging are less effective at capturing these complex relationships. Ignoring the audio track loses valuable information.
NEW QUESTION # 253
Consider the following code snippet used within a U-Net architecture. What is its purpose?
torch.cat ([up, skip], dim=1)
- A. It performs an element-wise addition of the 'up' and 'skip' tensors.
- B. It subtracts the 'skip' tensor from the 'up' tensor.
- C. It performs a matrix multiplication between the 'up' and 'skip' tensors.
- D. It concatenates the 'up' and 'skip' tensors along the channel dimension.
- E. It multiplies the 'up' and 'skip' tensors element-wise.
Answer: D
Explanation:
The 'torch.cat([up, skip], dim=1) function concatenates two tensors, 'up' and 'skip' , along the channel dimension (dim=1) In the context of a U-Net, 'up' represents the upsampled feature map from the decoder path, and 'skip' represents the corresponding feature map from the encoder path. Concatenating them allows the decoder to combine both coarse-grained and fine-grained information for better image reconstruction.
NEW QUESTION # 254
Consider a multimodal dataset containing text, images, and corresponding GPS coordinates. You want to build a model that predicts the sentiment of a social media post based on this dat a. Which of the following data preprocessing steps are crucial to ensure the model's performance and prevent data leakage?
- A. Resize all images to a uniform size.
- B. Normalize all text data to lowercase and remove punctuation.
- C. Standardize the GPS coordinates (latitude and longitude) using a scaler fitted only on the training data.
- D. Randomly shuffle the entire dataset before splitting it into training, validation, and test sets.
- E. Split the dataset into training, validation, and test sets based on time to avoid leakage of future information into the training set.
Answer: A,B,C,E
Explanation:
Normalizing text (A) and resizing images (B) are standard preprocessing steps. Time-based splitting (C) prevents data leakage by ensuring that the model is not trained on future data. Standardizing GPS coordinates (E) with training data prevents the test data from influencing the scaling. Random shuffling before splitting (D) can lead to data leakage in time-series data.
NEW QUESTION # 255
You are building a multimodal model for medical diagnosis that combines patient medical history (text), medical images (X-rays, MRIs), and sensor data (heart rate, blood pressure). The dataset contains significant amounts of missing data across all modalities. What strategy is most appropriate for handling the missing data and ensuring the model's robustness and accuracy?
- A. Using a Generative Adversarial Network(GAN) to impute missing values based on the other avalible modalities.
- B. Imputing missing values using simple methods like mean imputation or filling with a constant value.
- C. Removing all patients with missing data to create a clean dataset.
- D. Using a multimodal variational autoencoder (MVAE) to learn a joint latent representation of the data and impute missing values based on the observed modalities.
- E. Training seperate models for each avalible modality.
Answer: A,D
Explanation:
Removing patients with missing data can lead to a significant loss of information and bias the model. Simple imputation methods can introduce inaccuracies and fail to capture the relationships between modalities. Multimodal variational autoencoders (MVAEs) are specifically designed to handle missing data in multimodal datasets by learning a joint latent representation and imputing values based on the observed modalities. This approach is more robust and accurate than simple imputation methods. GAN can also be used to impute missing values.
NEW QUESTION # 256
You are building a multimodal generative A1 system that creates 3D models from text descriptions. The system produces accurate shapes but struggles to generate realistic textures and surface details. What approach would BEST address this limitation?
- A. Increase the batch size during the 3D model generation phase.
- B. Increase the number of parameters in the text encoder.
- C. Reduce the resolution of the generated 3D models to simplify the texture generation process.
- D. Train a separate texture generation network conditioned on the generated 3D shape.
- E. Add more layers to the shape decoder.
Answer: D
Explanation:
Training a separate texture generation network allows for specialization in generating realistic surface details. This approach decouples the shape generation from the texture generation, allowing each component to be optimized independently. The other options are less targeted at improving texture realism. Increasing text encoder parameters (A) improves text understanding, not texture quality. Reducing resolution (C) degrades the final output. Increasing batch size (D) affects training speed, not texture quality. Adding layers to the shape decoder (E) may improve shape accuracy, but not texture realism.
NEW QUESTION # 257
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