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NVIDIA Generative AI Multimodal Sample Questions (Q361-Q366):

NEW QUESTION # 361
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 output will always start with "The quick brown fox jumps over the lazy".
B. The parameter controls the number of different completion the model should return.
C. The GPT-2 model is guaranteed to generate grammatically correct and factually accurate text.
D. The output will always be exactly 50 tokens long.
E. The output will contain a single sequence of text generated by the GPT-2 model, starting with the provided prompt.

Answer: A,B,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 # 362
You are building a text-to-image generation pipeline using CLIP and a diffusion model. After training, you notice that the generated images often lack the specific details mentioned in the text prompts. Which of the following strategies could you employ to improve the alignment between text and image?

A. All of the above.
B. Increase the number of diffusion steps during the image generation process.
C. Fine-tune the CLIP model on a dataset of text-image pairs relevant to your desired domain.
D. Use negative prompt engineering to guide the diffusion process away from undesired attributes.
E. Increase the number of layers in the I-I-Net architecture of the diffusion model.

Answer: A

Explanation:
All the strategies mentioned can help improve the alignment between text and image. Increasing U-Net layers can improve image detail. Fine-tuning CLIP improves semantic understanding. Negative prompts refine image generation. More diffusion steps can improve image quality. All options contribute to better alignment.

NEW QUESTION # 363
You are building a multimodal application that needs to understand both image and text dat a. You want to use a pre-trained model but fine-tune it for your specific task. Which of the following strategies is MOST effective for fine-tuning a large pre-trained multimodal model?

A. Fine-tune only the image encoder layers, keeping the text encoder layers frozen.
B. Fine-tune only the text encoder layers, keeping the image encoder layers frozen.
C. Fine-tune the attention mechanism between the text and image encoders, while keeping the encoder weights frozen.
D. Train a new classification head from scratch on top of the frozen pre-trained model.
E. Fine-tune the entire model, including both text and image encoder layers, using a small learning rate.

Answer: E

Explanation:
Fine-tuning the entire model with a small learning rate allows the model to adapt to the specific nuances of the new task while leveraging the knowledge already learned during pre-training. Freezing layers can limit adaptability. Training only a new head might not fully utilize the pre-trained features.

NEW QUESTION # 364
Consider the following code snippet using a hypothetical Generative A1 library. This code is intended to generate an image from a text prompt and then refine it based on a user-provided style image. However, it's not producing the desired results. What is the MOST likely cause of the issue?

A. The 'strength' parameter in 'refine_image' is set too low, resulting in minimal stylistic changes.
B. The text prompt provided is too short.
C. The 'generate_image' function does not support the parameter.
D. The 'style_image' is not preprocessed correctly before being passed to the 'refine_image' function.
E. The library being used is incompatible with the GPU.

Answer: A

Explanation:
A low 'strength' value will result in the 'refine_image' function making only very subtle changes based on the 'style_image' While other factors could contribute, this is the most direct and likely cause of the observed behavior. The question doesn't present any issues related to preprocessing or library errors that would impact the other choices.

NEW QUESTION # 365
You are experimenting with different multimodal transformer architectures for a video understanding task. You are using a large pre- trained model and fine-tuning it on your specific dataset. You observe that the model is overfitting and struggling to generalize to unseen videos. Which of the following techniques would be most effective in mitigating overfitting in this scenario? (Choose two)

A. Use a smaller pre-trained model.
B. Reduce the number of transformer layers in the model.
C. Implement weight decay and dropout regularization.
D. Increase the batch size significantly.
E. Employ data augmentation techniques specifically designed for video data (e.g., temporal jittering, random cropping).

Answer: C,E

Explanation:
Weight decay and dropout are standard regularization techniques that help prevent overfitting. Data augmentation increases the diversity of the training data, improving the model's ability to generalize. Reducing the number of layers is a potentially viable option, but requires experimentation to achieve optimum performance.

NEW QUESTION # 366
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Quiz Valid NCA-GENM - NVIDIA Generative AI Multimodal Latest Exam Review

New NCA-GENM Latest Exam Review | High Pass-Rate NVIDIA NCA-GENM Free Study Material: NVIDIA Generative AI Multimodal

NVIDIA Generative AI Multimodal Sample Questions (Q361-Q366):

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