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NVIDIA Generative AI Multimodal Sample Questions (Q66-Q71):

NEW QUESTION # 66
You are tasked with integrating a CLIP model into your application to generate images based on text descriptions. You want to ensure that the generated images closely reflect the nuances of the text prompt. Which prompt engineering technique is MOST suitable for achieving this?

A. Using random prompts to explore the model's creative capabilities.
B. Using prompts consisting only of keywords related to the desired image.
C. Using overly verbose and descriptive prompts to maximize detail.
D. Using negative prompts to explicitly exclude unwanted features or styles.
E. Using short, concise prompts to minimize ambiguity.

Answer: D

Explanation:
Negative prompting is a powerful technique where you specify what you don't want in the generated image. This helps refine the output and steer the model away from undesirable artifacts or styles. For example, specifying "a futuristic city, but without flying cars".

NEW QUESTION # 67
Consider a scenario where you are evaluating the performance of a multimodal A1 model that generates descriptions for images. However, the generated descriptions tend to be repetitive and lack diversity. Which of the following techniques can be employed to address this issue and encourage more diverse and creative outputs from the model? (Select TWO)

A. Utilizing a temperature scaling parameter during decoding and increasing its value.
B. Employing a nucleus sampling (top-p sampling) decoding strategy.
C. Using a beam search decoding strategy with a small beam width.
D. Using a greedy decoding strategy.
E. Increasing the model's training data size.

Answer: A,B

Explanation:
Nucleus sampling (top-p sampling) randomly samples from the smallest set of words whose cumulative probability mass exceeds a threshold p, encouraging more diverse outputs. Increasing the temperature scaling parameter makes the probability distribution flatter, leading to more exploration and less predictable (more creative) outputs. Beam search with a small beam width might still result in repetitive outputs. Increasing the training data size can help, but it might not directly address the lack of diversity. Greedy decoding always selects the most probable word, leading to repetitive and predictable outputs.

NEW QUESTION # 68
You're analyzing the performance of a generative A1 model that produces images from text prompts. You notice that the model struggles to generate images with specific objects mentioned in the prompt, even though these objects appear frequently in the training dataset.
Which of the following techniques could BEST address this issue?

A. Implementing a re-weighting scheme that gives higher weight to training examples containing the problematic objects.
B. Applying a larger kernel size in the convolutional layers of the model.
C. Reducing the learning rate during training.
D. Increasing the batch size during training.
E. Adding more layers to the generative model's architecture.

Answer: A

Explanation:
Re-weighting the training data to emphasize examples containing the underrepresented objects directly addresses the issue of the model not learning these objects effectively. Increasing batch size or adding layers might improve overall performance but doesn't specifically target the object representation problem. Reducing learning rate may lead to slower convergence. Increasing kernel size changes receptive field but does not address the imbalanced representation of objects.

NEW QUESTION # 69
Which prompt engineering technique is most likely to improve the coherence and visual quality of images generated by a text-to-image model when generating complex scenes with multiple objects and intricate details?

A. Exclusively describing the background and neglecting foreground elements.
B. Using only abstract and ambiguous language.
C. Employing a negative prompt to specify elements to avoid.
D. Using short, concise prompts with only a few keywords.
E. Relying solely on the model's default style settings.

Answer: C

Explanation:
Employing a negative prompt allows you to explicitly instruct the model to avoid specific elements or artifacts that might degrade the visual quality of the generated image. This technique is particularly effective for complex scenes where you want to fine-tune the composition and appearance by preventing unwanted details or styles. Using shorter prompts or relying only on default styles is too general. Vague descriptions can lead to unpredictable outputs.

NEW QUESTION # 70
Consider this PyTorch code snippet related to processing multimodal dat a. What is the primary purpose of the following code in the context of Generative A1?

A. To concatenate image and text data into a single tensor.
B. To ensure images and text are processed in the same order during training.
C. To resize all images to the same dimension.
D. To create separate data loaders for images and text.
E. To create a custom dataset class for handling paired image and text data.

Answer: E

Explanation:
The code defines a custom dataset class ( 'ImageTextDataset' ) which is the standard way in PyTorch to handle datasets that involve paired data, such as images and corresponding text descriptions. This allows for efficient loading and processing of the data during training. The snippet does not directly concatenate, ensure order, or specifically resize the images, though these could be parts of the larger system built upon the dataset class. It also doesn't create separate data loaders, but allows to create one dataset class and loader for the multimodal data.

NEW QUESTION # 71
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Pass Guaranteed Quiz NVIDIA - NCA-GENM–Efficient Learning Materials

Quiz NVIDIA - NCA-GENM - NVIDIA Generative AI Multimodal Authoritative Learning Materials

NVIDIA Generative AI Multimodal Sample Questions (Q66-Q71):

Pass-Sure Learning NCA-GENM Materials, Latest NCA-GENM Test Pdf

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