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NVIDIA Generative AI Multimodal Sample Questions (Q360-Q365):

NEW QUESTION # 360
You're developing a text-to-image generation system using a pre-trained CLIP model and a diffusion model. You notice that while the generated images match the overall theme of the text prompt, they often fail to accurately represent specific objects mentioned in the prompt. What are the two MOST effective strategies to improve object fidelity in this scenario?

A. Fine-tune the diffusion model using a dataset of images specifically depicting the objects that are frequently misrepresented.
B. Increase the guidance scale during diffusion sampling, forcing the generated images to align more closely with the CLIP embeddings.
C. Implement a technique called 'Classifier-Free Diffusion Guidance', which allows for more flexible control over the generated image content.
D. All of the Above
E. Replace the CLIP model with a larger, more powerful text encoder that has been trained on a more diverse dataset.

Answer: B,C

Explanation:
Increasing the guidance scale (B) forces stronger alignment with the CLIP embeddings, improving object fidelity. Classifier-Free Diffusion Guidance (D) provides finer-grained control over image content, allowing the model to better represent specific objects. Fine-tuning the diffusion model (A) can be helpful but requires a significant amount of data. Using a larger text encoder (C) may improve overall performance but may not directly address object fidelity. Classifier-Free Diffusion Guidance and increasing guidance scale are the most targeted strategies to increase object fidelity for text-to-image models, as guidance scale can also have some artifacts.

NEW QUESTION # 361
You are working with a multimodal dataset containing medical images (X-rays) and corresponding patient reports (text). Some of the reports are missing or incomplete. Which of the following strategies would be most appropriate to handle this missing data in a multimodal AI model?

A. Using a multimodal autoencoder to reconstruct the missing reports from the available image data, or using a masked language model to predict missing words in the existing reports, conditioned on the image.
B. Training the model only on the complete data points and ignoring the incomplete ones.
C. Discarding all data points with missing reports.
D. Imputing the missing reports with a generic placeholder text.
E. Using a simple average of all available reports for imputation.

Answer: A

Explanation:
Using a multimodal autoencoder or a masked language model allows the model to leverage the relationship between the image and text modalities to infer the missing information. Discarding data or using simple imputation methods can lead to information loss or biased results. A multimodal autoencoder or masked language model can help to reconstruct the missing reports from the available image data, or using a masked language model to predict missing words in the existing reports, conditioned on the image.

NEW QUESTION # 362
A multimodal A1 model is designed to translate sign language videos into text. The model performs well on videos with clear hand gestures and lighting conditions but struggles with videos recorded in low light or with partial hand occlusions. Which of the following strategies would be MOST effective in improving the model's robustness to these challenging conditions?

A. Using a simpler text encoder.
B. Increasing the size of the text vocabulary.
C. Reducing the frame rate of the input videos.
D. Training the model on a smaller dataset.
E. Applying image enhancement techniques (e.g., contrast adjustment, noise reduction) to the video frames.

Answer: E

Explanation:
Applying image enhancement techniques to the video frames can improve the visibility of hand gestures in low-light conditions and reduce the impact of noise, making the model more robust. Reducing the frame rate or training on a smaller dataset would likely decrease performance. Increasing the text vocabulary or using a simpler text encoder would not directly address the issue of poor video quality.

NEW QUESTION # 363
You are building a multimodal generative model that combines text and images. The goal is to generate realistic images based on textual descriptions. You have access to a pre-trained language model (e.g., BERT) and a pre-trained image generation model (e.g., StyleGAN). Which of the following architectures would be MOST suitable for effectively integrating these two models to achieve your objective?

A. Training a separate neural network to map the image to the text description.
B. Using the language model to generate captions for the images, and then training the image generation model on the captions.
C. Using the language model to generate a latent vector that is then fed into the image generation model as input.
D. Concatenating the text and image data into a single vector and feeding it into a standard feedforward neural network.
E. Fine-tuning the language model to directly output pixel values for the image.

Answer: C

Explanation:
Using the language model to generate a latent vector that serves as input to the image generation model is an effective approach for multimodal integration. This allows the language model to encode the textual description into a meaningful representation that can guide the image generation process. Fine-tuning the language model to output pixel values directly is not feasible due to the high dimensionality of images. Training a separate network to map images to text is a reverse task. Concatenating text and image data may not effectively capture the complex relationships between modalities. Generating captions for images is not the primary objective.

NEW QUESTION # 364
You're developing a multimodal A1 system that takes image data, text descriptions, and user interaction data (clicks, dwell time) to generate personalized product recommendations. To effectively combine these modalities and capture complex relationships, which model architecture would be most suitable?

A. A deep learning architecture incorporating attention mechanisms and cross-modal fusion layers, with separate embedding layers for each modality, followed by a shared representation layer for joint learning and prediction.
B. A Naive Bayes classifier.
C. A k-nearest neighbors (KNN) algorithm.
D. A simple linear regression model.
E. A decision tree-based model.

Answer: A

Explanation:
Deep learning architectures with attention mechanisms and cross-modal fusion layers are best suited for capturing complex relationships between different modalities. Attention mechanisms allow the model to focus on the most relevant features from each modality, while cross-modal fusion layers enable joint learning and prediction based on the combined representations. Linear regression, decision trees, KNN, and Naive Bayes are less capable of capturing complex, non-linear relationships in multimodal data.

NEW QUESTION # 365
......

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Free PDF 2025 NVIDIA High Pass-Rate Official NCA-GENM Study Guide

NCA-GENM Exam Tutorial, NCA-GENM Valid Test Sample

NVIDIA Generative AI Multimodal Sample Questions (Q360-Q365):

2025 Official NCA-GENM Study Guide | Accurate 100% Free NVIDIA Generative AI Multimodal Exam Tutorial

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