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Latest NVIDIA NCA-GENM Questions in Three Different Formats

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NVIDIA Generative AI Multimodal Sample Questions (Q234-Q239):

NEW QUESTION # 234
You're building a generative A1 model that can create realistic 3D models from text descriptions. You have a dataset of text descriptions and corresponding 3D models, but the alignment between the text and the 3D models is weak. The model sometimes generates 3D shapes that don't accurately reflect the text. Which of the following techniques could improve the alignment between the text descriptions and the generated 3D models?

A. Using a pre-trained text encoder (e.g., BERT or CLIP) to extract meaningful features from the text descriptions.
B. Using a contrastive loss function that encourages the model to generate 3D models that are semantically similar to the corresponding text descriptions.
C. Increasing the number of vertices and faces in the 3D models.
D. Applying data augmentation techniques to the 3D models (e.g., random rotations and scaling).
E. Training the model with a larger batch size.

Answer: A,B

Explanation:
A contrastive loss function directly encourages the model to learn a mapping between text and 3D models that preserves semantic similarity. Using a pre-trained text encoder allows the model to leverage existing knowledge about language and extract more meaningful features from the text descriptions, improving alignment. Increasing the number of vertices and faces can improve the resolution of the models but won't directly address alignment. 3D data augmentation can improve robustness, but it's less direct. Batch size has a smaller impact compared to the other options.

NEW QUESTION # 235
Consider a scenario where you're integrating CLIP with a generative model to create images from text prompts. Which of the following best describes the primary role of CLIP in this process?

A. To encode text prompts into a vector representation that guides the image generation process.
B. To decode generated images back into text descriptions.
C. To act as a discriminator in a GAN setup.
D. To directly generate images based on text prompts.
E. To optimize the hyperparameters of the generative model.

Answer: A

Explanation:
CLIP (Contrastive Language-Image Pre-training) serves as an encoder to map text prompts into a vector space. This vector representation is then used to guide the generative model towards creating images that align with the semantic meaning of the text prompt. CLIP doesn't generate images directly, decode images to text or optimize hyperparameters.

NEW QUESTION # 236
You're working with a text-to-image generation model. After training, you notice the generated images lack fine-grained details and appear blurry. Which hyperparameter tuning strategy would be MOST effective in improving the visual quality of the generated images, considering the computational cost?

A. Adding more layers to the discriminator network (if using GANs).
B. Increasing the number of training epochs.
C. Decreasing the batch size.
D. Optimizing the learning rate schedule.
E. Switching to a different model architecture (e.g., from VAE to GAN).

Answer: D

Explanation:
Optimizing the learning rate schedule can have a significant impact on the quality of the generated images. A well-tuned learning rate can help the model converge to a better solution and avoid getting stuck in local minima. Increasing the number of training epochs may help, but also increases computational cost and can lead to overfitting. Adding more layers to the discriminator is a valid approach to consider if using GANs. While switching to a different architecture is an option, it would need to be justified by experimental results and may have other implications.

NEW QUESTION # 237
You're building a system to translate customer service chat logs into summaries that a human agent can quickly review The chat logs are often informal, contain slang, and have grammatical errors. Which prompt engineering technique is MOST likely to improve the quality and accuracy of the summaries generated by a large language model (LLM)?

A. Using a template prompt with predefined sections and keywords to guide the summarization process and ensure consistency across different chat logs.
B. Using a zero-shot prompt with a simple instruction like 'Summarize this chat log.'
C. Using a negative constraint prompt, explicitly stating what the LLM should not include in the summary (e.g., 'Do not include greetings or farewells.').
D. Using chain-of-thought prompting to encourage the LLM to explain its reasoning process before generating the summary.
E. Using a few-shot prompt with several examples of chat logs and their ideal summaries, explicitly demonstrating how to handle informality and errors.

Answer: A,C,D,E

Explanation:
Few-shot prompting provides the LLM with examples to learn from, allowing it to better handle the nuances of informal language and errors. Chain-of-thought helps the model reason step-by-step, leading to better summaries. Negative constraints prevent irrelevant information. Template prompts provide structure and consistency. A zero-shot prompt is less effective in this scenario due to the complexity of the input data.

NEW QUESTION # 238
You want to ensure that the system respects user privacy and avoids generating avatars that resemble real people without their consent.
Which of the following strategies would be MOST effective in addressing this ethical concern?

A. Adding watermarks to all generated avatars to indicate that they are A1-generated.to global minimum.
B. Generating all avatars with a cartoonish or stylized appearance to avoid any resemblance to real people.
C. Training the model on a dataset that includes images of real people with explicit consent.
D. Using a diverse and anonymized dataset for training and adding a mechanism to prevent the model from generating highly realistic facial features.
E. Implementing a face recognition system to detect and filter out avatars that closely resemble existing individuals.

Answer: D

Explanation:
Using a diverse and anonymized dataset reduces the risk of the model learning to generate avatars that are too similar to specific individuals. Preventing the generation of highly realistic facial features further mitigates this risk while still allowing for personalized avatars. Ethical concerns are paramount when developing generative A1 systems, particularly when dealing with potentially sensitive data such as facial features.

NEW QUESTION # 239
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