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NVIDIA Generative AI Multimodal Sample Questions (Q239-Q244):

NEW QUESTION # 239
You are tasked with deploying a generative A1 model for image inpainting using Triton Inference Server. The model takes an image with masked regions as input and outputs the completed image. You need to pre-process the input image before sending it to the server.
Which pre-processing steps are crucial for ensuring optimal performance and accuracy of the inpainting model?

A. Converting the image to grayscale.
B. Creating a binary mask indicating the regions to be inpainted.
C. Sharpening the image to enhance details.
D. Resizing the image to a fixed resolution and normalizing pixel values to a specific range (e.g., [0, 1] or [-1, 1]).
E. Applying data augmentation techniques like random rotations and flips.

Answer: B,D

Explanation:
Resizing and normalization ensure the input image conforms to the model's expected input size and data range. A binary mask is essential to indicate which regions the model should inpaint. Converting to grayscale would remove color information, hindering the inpainting process. Data augmentation should be done during the training phase. Sharpening is not typically a necessary pre-processing step for inpainting.

NEW QUESTION # 240
You are building a video summarization system that uses both visual (frame content) and audio (speech transcripts) information. You've noticed that the system tends to prioritize segments with clear speech but often misses important visual events that are not explicitly mentioned in the audio. How can you improve the system to better incorporate visual cues into the summarization process? (Select all that apply)

A. Train a separate model to detect important visual events (e.g., action recognition) and incorporate its output into the summarization process.
B. All of the above.
C. Use a multimodal attention mechanism that allows the model to attend to both visual and audio features simultaneously.
D. Fine-tune the audio transcription model to be more accurate.
E. Increase the weight given to audio features in the summarization model.

Answer: A,C

Explanation:
Training a separate model for visual event detection helps explicitly identify important visual cues. A multimodal attention mechanism allows the model to dynamically weigh the importance of visual and audio features. Increasing the weight of audio features would exacerbate the problem. While fine-tuning the audio model is helpful, it doesn't address the core issue of incorporating visual cues. Option E is incorrect as B is incorrect.

NEW QUESTION # 241
Consider the following code snippet used in training a multimodal model:

During experimentation, you discover that the image modality contributes negligibly to the final prediction. How would you modify the training loop to dynamically adjust the importance of each modality?

A. Apply a fixed weight to the image features before feeding them into the model.
B. Use a curriculum learning approach where the model is initially trained only on the text modality, and the image modality is gradually introduced.
C. Introduce a modality dropout mechanism that randomly drops either the image or text modality during each training iteration.
D. Implement a separate loss function for the image modality and adjust its weight based on validation performance.
E. Compute modality-specific gradients and apply a scaling factor to the image gradients based on their magnitude relative to the text gradients.

Answer: E

Explanation:
Dynamically scaling gradients based on their magnitude allows the model to automatically adjust the importance of each modality during training. If the image gradients are small compared to the text gradients, the scaling factor will increase their influence, encouraging the model to learn from the image modality. Modality dropout is helpful, however gradient scaling provides finer control.

NEW QUESTION # 242
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. Training a custom text encoder from scratch with a larger dataset specifically tailored to your application's domain.
B. Increasing the temperature parameter of the diffusion model used in conjunction with CLIP to introduce more randomness and potentially more detail.
C. Applying prompt engineering techniques such as adding descriptive adjectives and context to the text prompt and fine-tuning the prompt with iterative feedback.
D. Using a larger image decoder network with more parameters to add detail during the image generation process.
E. Reducing the number of training steps for the diffusion model to prevent overfitting to the training data and promote generalization.

Answer: C

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 # 243
You are evaluating a Generative A1 model for image captioning. Which of the following metrics is MOST appropriate for assessing the semantic similarity between the generated captions and the ground truth captions?

A. ROUGE score
B. Perplexity
C. Inception Score
D. CIDEr score
E. BLEU score

Answer: D

Explanation:
CIDEr (Consensus-based Image Description Evaluation) is specifically designed for image captioning and is highly correlated with human judgments of caption quality. While BLEU and ROUGE are useful for general text generation, CIDEr excels at capturing semantic similarity in image captions. Inception Score assesses the quality of generated images, not captions, and Perplexity measures the uncertainty of a language model.

NEW QUESTION # 244
......

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New NCA-GENM Test Pass4sure - Free PDF Quiz First-grade NVIDIA New NCA-GENM Test Papers

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NVIDIA Generative AI Multimodal Sample Questions (Q239-Q244):

Free PDF Quiz 2025 NCA-GENM: NVIDIA Generative AI Multimodal Marvelous New Test Pass4sure

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