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NVIDIA Generative AI Multimodal Sample Questions (Q235-Q240):

NEW QUESTION # 235
Which of the following techniques are MOST effective for improving the energy efficiency of a large-scale Generative A1 model during inference, while minimizing performance degradation?

A. Increasing the batch size significantly
B. Model quantization (e.g., INT8)
C. Gradient accumulation
D. Knowledge distillation to a smaller model
E. Pruning (removing less important weights)

Answer: B,D,E

Explanation:
Model quantization reduces the memory footprint and computational cost by representing weights with fewer bits. Knowledge distillation trains a smaller, faster model to mimic the behavior of a larger model. Pruning removes redundant connections, reducing the number of computations. Gradient accumulation is for training, not inference. Increasing batch size may improve throughput but not necessarily energy efficiency per sample and might even decrease it due to increased memory usage.

NEW QUESTION # 236
You're tasked with building a system that can generate realistic images from text descriptions and, conversely, generate accurate text descriptions from images. You decide to use a GAN (Generative Adversarial Network) architecture, but need to handle both modalities effectively. What GAN variant would be MOST suitable for this bi-directional multimodal task?

A. Deep Convolutional GAN (DCGAN)
B. Super-Resolution GAN (SRGAN)
C. Conditional GAN (cGAN)
D. CycleGAN
E. Vanilla GAN

Answer: D

Explanation:
CycleGAN is designed for unpaired image-to-image translation. In this scenario, it can be adapted to translate between the image and text modalities without requiring paired data. One generator learns to generate images from text, while another learns to generate text from images. Cycle consistency ensures that translating an image to text and then back to an image results in an image similar to the original. Vanilla GANI cGAN, and DCGAN are not inherently designed for bi- directional translation between modalities without paired data. SRGAN is for image super-resolution.

NEW QUESTION # 237
Consider the following code snippet intended to generate an image embedding using CLIP. What is the most likely reason for the 'RuntimeErroN?

A. The image tensor does not require gradient calculation.
B. The image is not in RGB format.
C. The image size is not compatible with the CLIP model's input requirements.
D. The image pixel values are not normalized correctly.
E. The CLIP model was not properly loaded onto the GPIJ.

Answer: C

Explanation:
CLIP models typically require images to be resized to a specific dimension (e.g., 224x224). The 'RuntimeError' suggests a size mismatch. The provided code snippet, though not complete, doesn't explicitly resize the image before passing it to the model.

NEW QUESTION # 238
You're evaluating the performance of a video captioning model. The model generates captions for video clips. You notice that while the captions are generally accurate, they often lack detail and creativity. Which metric(s) would be MOST suitable for assessing the diversity and originality of the generated captions? (Select all that apply)

A. BLEU (Bilingual Evaluation Understudy)
B. CIDEr (Consensus-based Image Description Evaluation)
C. Self-BLEU
D. ROUGE (Recall-Oriented Understudy for Gisting Evaluation)
E. SPICE (Semantic Propositional Image Caption Evaluation)

Answer: C

Explanation:
Self-BLEU calculates the BLEU score of a set of generated captions against themselves. A lower Self-BLEU score indicates higher diversity, as it means the captions are less similar to each other. The other metrics (BLEU, CIDEr, SPICE, ROUGE) primarily focus on accuracy and semantic similarity to reference captions, not diversity. Although, SPICE could capture the semantic similarity of different captions, Self-BLEU is most used.

NEW QUESTION # 239
You are building a multimodal application that takes an image and a short text description as input and generates a more detailed text description of the image. Which of the following model architectures is BEST suited for this task?

A. A Multilayer Perceptron (MLP) for both image and text.
B. A Recurrent Neural Network (RNN) with attention mechanisms.
C. A simple CNN followed by an LSTM.
D. A Generative Adversarial Network (GAN) with separate image and text encoders.
E. A Vision Transformer (ViT) for image encoding and a Transformer for text decoding.

Answer: E

Explanation:
A Vision Transformer (ViT) excels at encoding image information, and a Transformer architecture is highly effective for text generation. The combination allows for effective processing of both modalities and generation of coherent, detailed text descriptions based on the image content and initial text prompt. CNN+LSTM could work, but is generally less performant. RNNs struggle with long-range dependencies. GANs are not ideal for this specific text generation task. MLPs don't capture the sequential dependencies well.

NEW QUESTION # 240
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