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NVIDIA Generative AI Multimodal Sample Questions (Q112-Q117):

NEW QUESTION # 112
You are training a variational autoencoder (VAE) for generating images. The generated images are blurry and lack fine details. Which of the following modifications to the VAE architecture or training procedure would be MOST effective in improving the image quality?

A. Using a larger batch size.
B. Decreasing the value of the KL divergence weight in the loss function.
C. Increasing the capacity of the encoder and decoder networks.
D. Using a deeper latent space (higher dimensionality).
E. Using a smaller batch size.

Answer: B

Explanation:
Decreasing the KL divergence weight allows the model to focus more on reconstructing the input images and less on forcing the latent space to conform to a standard normal distribution. This can lead to sharper, more detailed images. Increasing the capacity of the networks or the dimensionality of the latent space may help, but only if the KL divergence weight is appropriately adjusted. Larger or smaller batch sizes do not directly influence the blurriness problem.

NEW QUESTION # 113
You are tasked with building a multimodal generative A1 model that takes both image and text as input to generate a coherent video. Which of the following architectures is MOST suitable for this task, considering the need to fuse information from different modalities and generate sequential data?

A. A Transformer-based architecture with separate encoders for image and text, followed by a decoder that generates video frames.
B. A Support Vector Machine (SVM) classifier trained to predict the next frame based on image and text features.
C. A Generative Adversarial Network (GAN) trained solely on image data and later fine-tuned with text embeddings.
D. A standard Convolutional Neural Network (CNN) followed by a fully connected layer.
E. A simple recurrent neural network (RNN) that concatenates image feature vectors and text embeddings as input at each time step.

Answer: A

Explanation:
Transformer-based architectures are well-suited for multimodal tasks as they can effectively fuse information from different modalities through attention mechanisms. The separate encoders can handle image and text data, and the decoder can generate the video frames sequentially CNNs and RNNs alone may struggle with long-range dependencies, and GANs might not directly incorporate textual information during video generation. SVMs are classifiers, not generative models.

NEW QUESTION # 114
A multimodal A1 model is trained on a dataset containing biased text and images. This bias leads to the model generating outputs that reinforce negative stereotypes. Which of the following steps are crucial for addressing and mitigating this bias during the model development lifecycle? (Select TWO)

A. Implementing model distillation to reduce the model size
B. Increasing the learning rate during training.
C. Reducing the number of layers in the neural network.
D. Collecting a more diverse and representative dataset.
E. Using adversarial training techniques to encourage fairness.

Answer: D,E

Explanation:
Collecting a more diverse dataset helps to reduce the bias present in the training data. Adversarial training can be used to train the model to be more robust to biased inputs and generate fairer outputs. Increasing the learning rate or reducing the number of layers do not directly address bias. Model distillation mainly aims at model size reduction, not bias mitigation.

NEW QUESTION # 115
A financial institution is developing a multimodal A1 system to detect fraudulent transactions by analyzing transaction details (text), user images, and audio recordings of phone calls. Which of the following strategies is MOST crucial for handling the missing data that frequently occurs across these modalities?

A. Ignoring transactions with missing data to simplify the model's training process.
B. Employing a joint imputation approach that leverages information from available modalities to predict and fill in missing values in other modalities.
C. Replacing missing data with a single, arbitrary placeholder value (e.g., -1 for numerical data, 'missing' for text) across all modalities.
D. Imputing missing data in each modality independently using modality-specific imputation techniques (e.g., mean imputation for numerical data, most frequent category for categorical data).
E. Using a modality dropout technique during training, randomly masking modalities to force the model to learn robust representations from incomplete data.

Answer: B,E

Explanation:
Ignoring missing data or using simple imputation techniques can introduce bias and reduce the model's accuracy. A joint imputation approach is superior because it leverages the relationships between modalities to improve imputation accuracy. Modality dropout during training further enhances robustness to missing data in real-world scenarios.

NEW QUESTION # 116
You are tasked with optimizing a multimodal A1 model that processes both image and text data for generating image captions. The model exhibits slow inference times, particularly when handling high-resolution images. Which of the following optimization strategies would be MOST effective in reducing inference latency, considering the NVIDIA ecosystem?

A. Switching to a larger model architecture with more parameters.
B. Removing dropout layers from the model.
C. Using a simpler loss function during training.
D. Increasing the batch size during inference to better utilize GPU resources.
E. Implementing TensorRT for model optimization and quantization.

Answer: E

Explanation:
TensorRT is specifically designed for optimizing deep learning models for inference on NVIDIA GPUs. It performs graph optimization, quantization, and kernel fusion to reduce latency and increase throughput. Increasing batch size can sometimes help, but may also lead to memory issues or increased latency for small batch sizes. A larger model will generally increase latency. Simpler loss functions and removing dropout affect training and generalization, not necessarily inference speed.

NEW QUESTION # 117
......

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