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NVIDIA Generative AI Multimodal Sample Questions (Q26-Q31):

NEW QUESTION # 26
You are building an image generation pipeline that leverages both a U-Net and a pre-trained CLIP model. After generating an image with the U-Net, you want to use CLIP to assess how well the generated image aligns with a given text prompt. Which of the following steps are crucial for obtaining a meaningful similarity score between the image and the text using CLIP?

A. Calculate the cosine similarity between the image and text embeddings.
B. Encode the text prompt using CLIP's text encoder.
C. Encode the generated image using CLIP's image encoder.
D. Resize the generated image to a very high resolution.
E. Fine-tune the CLIP model on your specific image generation task.

Answer: A,B,C

Explanation:
To assess the alignment between a generated image and a text prompt using CLIP, you need to encode both the image and the text into vector representations using CLIP's respective encoders (image and text encoders). Then, calculate the cosine similarity between these embeddings to quantify their semantic relatedness. Fine-tuning CLIP is not typically necessary for this purpose. High resolution is not mandatory as CLIP works well on medium resolution images and it's embedded space.

NEW QUESTION # 27
When deploying a large multimodal model to a resource-constrained environment (e.g., an edge device), which optimization techniques are MOST crucial to consider? (Select all that apply)

A. Increasing the batch size to improve throughput.
B. Knowledge distillation to transfer knowledge from a larger, more accurate model to a smaller, faster model.
C. Adding more layers to the model to improve accuracy.
D. Pruning to remove less important connections from the model.
E. Model quantization to reduce the model's memory footprint and computational requirements.

Answer: B,D,E

Explanation:
Model quantization, knowledge distillation, and pruning are all effective techniques for reducing the size and computational cost of a model, making it suitable for deployment in resource-constrained environments. Increasing the batch size would typically increase the memory usage. Adding layers would only increase the size.

NEW QUESTION # 28
You are working on a project to classify images of different types of flowers. You have a relatively small dataset (around 500 images per class). Which of the following techniques would be the MOST effective to improve the performance of your image classifier, considering the limited data?

A. Apply aggressive data augmentation techniques, such as random rotations, flips, and crops.
B. Train a very deep convolutional neural network from scratch-
C. Use a pre-trained convolutional neural network on a large dataset like ImageNet and fine-tune it on your flower dataset
D. Use a simple linear classifier.
E. Reduce the image resolution to decrease the number of parameters in the model.

Answer: C

Explanation:
Transfer learning, specifically fine-tuning a pre-trained model, is highly effective when dealing with small datasets. Pre-trained models have already learned useful features from large datasets, and fine-tuning them allows the model to adapt to the specific characteristics of your flower dataset. Training a deep network from scratch with limited data will likely lead to overfitting. Data augmentation helps, but transfer learning is generally more impactful. Reducing image resolution might lose important details, and a linear classifier might be too simple to capture the complexity of image features.

NEW QUESTION # 29
You are working on a project that involves generating realistic images of furniture based on textual descriptions. The input data consists of text descriptions and a small dataset of existing furniture images. Which data augmentation techniques would be MOST effective in improving the quality and diversity of the generated images?

A. Using generative adversarial networks (GANs) to generate new furniture images from the existing dataset.
B. Randomly cropping and rotating the existing furniture images.
C. Synthesizing new text descriptions using paraphrasing and back-translation techniques.
D. Focusing solely on increasing the size of the text dataset and ignoring image augmentation.
E. Combining A, B, and C.

Answer: E

Explanation:
Combining all techniques provides the best results. Image augmentations like cropping and rotation increase the variance of the image data. GANs create entirely new images, and text augmentation enhances the diversity of the input descriptions. Focusing only on one modality will likely limit the model's performance.

NEW QUESTION # 30
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 a simple feedforward network to combine the ResNet and Wav2Vec features.
B. Concatenating the ResNet and Wav2Vec features and feeding them into a single LSTM.
C. Training separate LSTMs for visual and audio features and averaging their outputs.
D. Ignoring the audio track and only using the video frames.
E. Using cross-attention mechanisms where the audio features attend to the visual features, and vice-versa, before feeding them into a Transformer decoder.

Answer: E

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 # 31
......

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