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NVIDIA Generative AI Multimodal Sample Questions (Q388-Q393):

NEW QUESTION # 388
You are building a multi-modal model that combines text and image data for a search application. The goal is to retrieve relevant images given a text query. You have encoded both images and text into embeddings. What's a suitable loss function for training the model to ensure images relevant to a text query are ranked higher than irrelevant ones?

A. Contrastive Loss
B. KL Divergence
C. Triplet Loss
D. Mean Squared Error (MSE)
E. Cross-entropy loss

Answer: C

Explanation:
Triplet Loss is specifically designed for ranking tasks. It takes three inputs: an anchor (text query), a positive example (relevant image), and a negative example (irrelevant image). The loss function aims to minimize the distance between the anchor and the positive example while maximizing the distance between the anchor and the negative example. Contrastive loss works with pairs, not relative rankings. Cross-entropy, MSE, and KL Divergence are not suitable for ranking problems.

NEW QUESTION # 389
Consider the following Python code snippet that utilizes a pre-trained language model from the Hugging Face Transformers library:

Which of the following statements are TRUE regarding the generated output?

A. The parameter controls the number of different completion the model should return.
B. The GPT-2 model is guaranteed to generate grammatically correct and factually accurate text.
C. The output will contain a single sequence of text generated by the GPT-2 model, starting with the provided prompt.
D. The output will always be exactly 50 tokens long.
E. The output will always start with "The quick brown fox jumps over the lazy".

Answer: A,C,E

Explanation:
The code uses the Hugging Face Transformers pipeline to generate text using the GPT-2 model. The 'max_length' parameter sets the maximum length of the generated sequence, but the model may stop generating earlier if it reaches a natural stopping point. num_return_sequences' controls the number of sequences that return. Pre-trained language models are not guaranteed to be grammatically perfect or factually accurate. The output always includes the prompt.

NEW QUESTION # 390
You are training a text-to-image diffusion model and observe that the generated images often exhibit a 'washed-out' or overly smooth appearance. Which of the following adjustments to the training process would likely improve the image quality and detail?

A. Reduce the batch size used during training to minimize memory consumption.
B. Reduce the learning rate for the U-Net architecture within the diffusion model.
C. Apply more aggressive data augmentation techniques to the training dataset.
D. Decrease the number of diffusion steps used during training.
E. Increase the weight of the perceptual loss function in the training objective.

Answer: E

Explanation:
A perceptual loss function encourages the generated images to have more realistic features and details, as it compares the high- level representations of the generated images to the real images. Increasing its weight in the training objective would incentivize the model to produce more detailed and visually appealing results. Decreasing diffusion steps leads to faster but often lower-quality results. Reducing batch size can affect training stability but doesn't directly address the 'washed-out' appearance. Data augmentation and learning rate adjustments may have some impact, but are less directly targeted at improving image detail.

NEW QUESTION # 391
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 simple recurrent neural network (RNN) that concatenates image feature vectors and text embeddings as input at each time step.
B. A standard Convolutional Neural Network (CNN) followed by a fully connected layer.
C. A Generative Adversarial Network (GAN) trained solely on image data and later fine-tuned with text embeddings.
D. A Transformer-based architecture with separate encoders for image and text, followed by a decoder that generates video frames.
E. A Support Vector Machine (SVM) classifier trained to predict the next frame based on image and text features.

Answer: D

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 # 392
You are tasked with optimizing a multimodal A1 model that processes both images and text. You observe significant latency during the image encoding phase using a pre-trained ResNet50 model. Which of the following techniques would be MOST effective in reducing latency while preserving accuracy, considering energy efficiency?

A. Increase the batch size for image processing.
B. Apply knowledge distillation, training a smaller, faster model to mimic the ResNet50 output.
C. Replace ResNet50 with a larger, more complex model like ResNeXt101.
D. Disable GPU acceleration for image processing to reduce power consumption.
E. Use full precision floating point operations throughout the ResNet50 model.

Answer: B

Explanation:
Knowledge distillation involves training a smaller, more efficient model to approximate the behavior of a larger, more accurate model. This can significantly reduce latency without a major drop in accuracy. Increasing batch size (A) may increase throughput but doesn't necessarily reduce latency per image. Replacing with a larger model (C) will increase latency and power consumption. Using full precision (D) is less energy-efficient than using mixed precision or quantization. Disabling GPU acceleration (E) would drastically increase latency.

NEW QUESTION # 393
......

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