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NVIDIA Generative AI Multimodal Sample Questions (Q194-Q199):

NEW QUESTION # 194
Consider a scenario where you're building a multimodal model to generate image captions. You've pre-trained a large language model (LLM) on a massive text corpus and a convolutional neural network (CNN) on ImageNet. How would you effectively combine these pre- trained components for your image captioning task, considering the need to maintain high caption quality and training efficiency?

A. Freeze the LLM, train the CNN to predict text embeddings, and then decode these embeddings into captions.
B. Freeze the CNN, extract image features, and train the LLM to generate captions from these features.
C. Fine-tune both the CNN and the LLM jointly on the image captioning dataset.
D. Use a transformer-based encoder to process both image features and text embeddings before feeding them to the LLM decoder.
E. Train the CNN and LLM separately on unrelated datasets and then combine them at inference time using a simple averaging of their outputs.

Answer: C,D

Explanation:
Fine-tuning both the CNN and LLM jointly allows the model to adapt both visual feature extraction and language generation to the specific task of image captioning, leading to potentially higher quality captions. However, this can be computationally expensive. Using a transformer-based encoder to process both modalities before the LLM decoder allows for effective cross-modal attention and fusion, which is also a strong approach. Freezing either the CNN or LLM limits the model's ability to adapt. Training separately and averaging outputs is unlikely to produce coherent captions.

NEW QUESTION # 195
You are evaluating a Generative A1 model for image captioning. Which of the following metrics is MOST appropriate for assessing the semantic similarity between the generated captions and the ground truth captions?

A. CIDEr score
B. BLEU score
C. Perplexity
D. Inception Score
E. ROUGE score

Answer: A

Explanation:
CIDEr (Consensus-based Image Description Evaluation) is specifically designed for image captioning and is highly correlated with human judgments of caption quality. While BLEU and ROUGE are useful for general text generation, CIDEr excels at capturing semantic similarity in image captions. Inception Score assesses the quality of generated images, not captions, and Perplexity measures the uncertainty of a language model.

NEW QUESTION # 196
When experimenting with different architectures for a text-to-image model, you observe that a Diffusion model generates higher quality images than a GAN (Generative Adversarial Network). However, the Diffusion model is significantly slower to generate images. What strategy can you employ to improve the inference speed of the Diffusion model without significantly sacrificing image quality?

A. Train the GAN for a longer duration.
B. Use a larger UNet architecture within the Diffusion model.
C. Use a smaller batch size.
D. Increase the number of diffusion steps.
E. Employ distillation techniques to train a faster, smaller model.

Answer: E

Explanation:
Model distillation involves training a smaller, faster 'student' model to mimic the behavior of a larger, slower 'teacher' model. This allows you to retain much of the quality of the original model while significantly improving inference speed. Increasing the number of diffusion steps or using a larger UNet would further slow down the Diffusion model. Training the GAN longer doesn't address the speed issue of the Diffusion model. Using a smaller batch size might help with memory limitations, but won't significantly improve inference speed.

NEW QUESTION # 197
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. Removing dropout layers from the model.
B. Switching to a larger model architecture with more parameters.
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 # 198
You're training a generative adversarial network (GAN) for multimodal image synthesis. The GAN takes text descriptions as input and generates corresponding images. You observe that the generator consistently produces images that are semantically related to the text but lack fine-grained details.
Which of the following loss functions, when combined with the standard GAN loss, would be MOST effective in improving the image quality and realism?

A. Cross-entropy loss between the generated image and the text description.
B. Cosine Similarity loss between generated image and a real image.
C. Perceptual loss based on features extracted from a pre-trained convolutional neural network (CNN).
D. L1 loss between the generated image and the text embedding.
E. Mean Squared Error (MSE) loss between the generated image and a real image from the training set.

Answer: C

Explanation:
Perceptual loss leverages pre-trained CNNs to capture high-level image features and style information, encouraging the generator to produce more realistic and visually appealing images. MSE can blur images, and cross-entropy between image and text is not directly applicable. L1 with text embedding is nonsensical as dimensions don't match. Cosine Similarity is rarely used for images.

NEW QUESTION # 199
......

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NVIDIA Generative AI Multimodal Sample Questions (Q194-Q199):

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