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NVIDIA Generative AI Multimodal Sample Questions (Q101-Q106):

NEW QUESTION # 101
You are building a multimodal Generative AI model that takes text and images as input to generate a story. The text encoder uses a pre-trained BERT model, and the image encoder uses a pre-trained ResNet50 model. What is the BEST strategy to align the feature spaces of these two encoders during training to ensure effective multimodal fusion?

A. Concatenate the outputs of BERT and ResNet50 directly without any alignment strategy.
B. Fine-tune only the BERT model while keeping the ResNet50 model frozen.
C. Fine-tune only the ResNet50 model while keeping the BERT model frozen.
D. Train a separate linear projection layer for each encoder and minimize the LI distance between the projected features. Freeze BERT and ResNet50.
E. Use a contrastive loss function that encourages similar representations for semantically related text and images, and dissimilar representations otherwise. Fine-tune BERT and ResNet50.

Answer: E

Explanation:
Contrastive learning is a powerful technique for aligning feature spaces in multimodal learning. By encouraging similar representations for semantically related inputs and dissimilar representations for unrelated inputs, it allows the model to learn a shared representation space that facilitates effective fusion. Fine- tuning both encoders allows for adaptation to the specific task. Other methods are less effective for aligning high-dimensional feature spaces from different modalities.

NEW QUESTION # 102
Consider the following Python code snippet using PyTorch. What does this code do in the context of data preprocessing for a Generative AI model?

Answer: A

Explanation:
The code snippet first resizes the images to a fixed size (256x256). Then, it converts the images into PyTorch tensors, which are the standard data format for PyTorch models. Finally, it normalizes the pixel values to a range of approximately [-1, 1]. This normalization helps to improve the training stability and performance of the generative A1 model by scaling the input values.

NEW QUESTION # 103
Which of the following are valid methods for addressing the vanishing gradient problem in deep neural networks?

A. Using batch normalization.
B. Employing skip connections (e.g., in ResNets).
C. Increasing the learning rate.
D. Using ReLU (Rectified Linear Unit) activation functions.
E. Using sigmoid activation functions.

Answer: A,B,D

Explanation:
ReLU avoids saturation like sigmoid, helping gradients flow. Skip connections provide alternative pathways for gradients. Batch normalization stabilizes learning and can help mitigate vanishing gradients. Increasing learning rate is unrelated, and sigmoid exacerbates the problem due to saturation.

NEW QUESTION # 104
You are building a text-to-image application using CLIP. You notice that the generated images often lack specific details mentioned in the text prompt. Which of the following techniques would be most effective in improving the fidelity and detail of the generated images, given the limitations of CLIP's text encoder?

A. Using a larger image decoder network with more parameters to add detail during the image generation process.
B. Reducing the number of training steps for the diffusion model to prevent overfitting to the training data and promote generalization.
C. Training a custom text encoder from scratch with a larger dataset specifically tailored to your application's domain.
D. Applying prompt engineering techniques such as adding descriptive adjectives and context to the text prompt and fine-tuning the prompt with iterative feedback.
E. Increasing the temperature parameter of the diffusion model used in conjunction with CLIP to introduce more randomness and potentially more detail.

Answer: D

Explanation:
Prompt engineering is the most practical and effective method for improving the fidelity of text-to-image generation with CLIP, without requiring extensive retraining or architecture changes. By carefully crafting and refining the text prompt, you can guide the generation process to produce images that more accurately reflect the desired details. Training a custom text encoder (A) is resource-intensive. While a larger image decoder (B) might help, it doesn't address the core issue of accurately capturing the prompt's meaning. Increasing temperature (D) can add randomness but not necessarily detail. Reducing training steps (E) could worsen performance.

NEW QUESTION # 105
You are building a multimodal generative A1 application that uses CLIP to align text and image embeddings. You observe that the generated images lack detail and fidelity to the text prompt. Which of the following strategies would be MOST effective in improving image quality, and how could prompt engineering and Triton Inference Server play a role?

A. Refining the text prompts to be more descriptive and specific, incorporating stylistic details and relevant keywords. Triton can optimize the prompt embedding process.
B. Training a separate image super-resolution model to enhance the generated images after they are produced by the CLIP-guided generator. Triton can manage the concurrent execution of the generator and super-resolution models.
C. All of the above
D. Using a larger batch size during CLIP training and increasing the learning rate. Triton is not directly involved in model training.
E. Increasing the CLIP model's text encoder's hidden layer size and using more aggressive data augmentation during CLIP training. Triton can be used to serve the augmented CLIP model at scale.

Answer: A,B

Explanation:
Refining text prompts (B) with prompt engineering is crucial for guiding the generative process toward desired outputs. A more specific prompt provides better guidance for the image generator. Using a separate super-resolution model (C) addresses the detail issue directly. While increasing CLIP's capacity (A) could help, it's less direct than prompt engineering or super-resolution and may be computationally expensive. Larger batch sizes and learning rates (D) are training parameters and do not directly address the image quality problem after training. Triton's role in serving various models (CLIP, generator, super-resolution) concurrently is key to a seamless pipeline. Therefore both B and C are the most effective strategies.

NEW QUESTION # 106
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Free PDF Quiz 2025 Efficient NCA-GENM: Exam Vce NVIDIA Generative AI Multimodal Free

NCA-GENM valid training questions & NCA-GENM updated practice vce & NCA-GENM exam cram test

NVIDIA Generative AI Multimodal Sample Questions (Q101-Q106):

100% Pass-Rate Exam Vce NCA-GENM Free – Correct New Exam Objectives for NCA-GENM

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