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NVIDIA Generative AI Multimodal Sample Questions (Q272-Q277):

NEW QUESTION # 272
You are deploying a Riva-based speech-to-text service in a production environment. You observe high latency and CPU utilization on your server Which of the following actions would be most effective in optimizing the performance of your Riva service?

A. Increasing the audio chunk size sent to the Riva server to reduce the number of requests.
B. Deploying the Riva server on a CPU-only instance to reduce cost.
C. Switching to a smaller, less accurate ASR model to reduce computational load.
D. Enabling batching and concurrency in the Riva server configuration to process multiple requests simultaneously.
E. Disabling automatic punctuation and capitalization to simplify the ASR process.

Answer: D

Explanation:
Enabling batching and concurrency is a key optimization strategy for Riva. It allows the server to process multiple audio streams simultaneously, maximizing GPU utilization and reducing overall latency. Switching to a smaller model (A) might reduce load but also decreases accuracy. Disabling punctuation (C) has a minor impact. Increasing audio chunk size (D) can help, but batching is more significant. Deploying on CPU (E) negates the benefits of Riva's GPU acceleration.

NEW QUESTION # 273
You're building a chatbot that can understand both text and images. The chatbot is intended to answer questions about images uploaded by users. However, you observe that when presented with complex scenes containing multiple objects, the chatbot struggles to accurately identify and describe the objects being queried. Which of the following strategies would be MOST beneficial in improving the chatbot's performance on complex visual scenes?

A. Integrate an object detection model to identify and localize objects in the image before feeding the information to the chatbot.
B. Remove the image processing component entirely.
C. Use a larger language model for the chatbot.
D. Reduce the resolution of the input images.
E. Train the chatbot on a dataset with only simple images containing a single object.

Answer: A

Explanation:
Integrating an object detection model allows the chatbot to explicitly identify and localize the objects within the image, providing crucial information for answering questions about specific objects in complex scenes. A larger language model can help with general language understanding, but doesn't address the fundamental issue of object identification. Reducing image resolution or training on simple images will degrade performance on complex scenes. Removing image processing defeats the purpose.

NEW QUESTION # 274
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 negative prompts to explicitly exclude unwanted features or styles.
B. Using random prompts to explore the model's creative capabilities.
C. Using prompts consisting only of keywords related to the desired image.
D. Using overly verbose and descriptive prompts to maximize detail.
E. Using short, concise prompts to minimize ambiguity.

Answer: A

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 # 275
You are building a Generative Adversarial Network (GAN) to generate high-resolution images. The generated images suffer from mode collapse, where the generator only produces a limited variety of images. Which of the following techniques would be MOST effective in mitigating mode collapse?

A. Using a simpler generator architecture.
B. Decreasing the learning rate of the discriminator.
C. Using mini-batch discrimination or feature matching in the discriminator.
D. Reducing the size of the generator's latent space.
E. Increasing the learning rate of the generator.

Answer: C

Explanation:
Mini-batch discrimination and feature matching encourage the generator to produce diverse images by considering the relationships between generated samples in a batch. Adjusting learning rates may have some effect but doesn't directly address the diversity issue. Reducing the latent space or using a simpler generator would likely reduce the generator's capacity and exacerbate mode collapse.

NEW QUESTION # 276
You are working on a project that involves generating realistic images from text descriptions using a diffusion model. You want to reduce the inference time of the model, which currently takes several minutes to generate a single image. Which of the following techniques would be MOST effective for accelerating inference without significantly compromising image quality?

A. Increasing the number of diffusion steps.
B. Employing techniques like DDIM (Denoising Diffusion Implicit Models) or progressive distillation to reduce the number of sampling steps required.
C. Using a smaller batch size during inference.
D. Training the diffusion model with a larger dataset.
E. Switching to a CPU-based inference engine.

Answer: B

Explanation:
DDIM and progressive distillation are specifically designed to reduce the number of sampling steps needed in diffusion models, leading to faster inference. Increasing diffusion steps (A) would increase inference time. Smaller batch size (B) might reduce memory usage, but not significantly affect overall inference time. Training with a larger dataset (D) improves quality, but not inference speed. Switching to CPU (E) would dramatically slow down inference.

NEW QUESTION # 277
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100% Pass 2025 Newest NVIDIA NCA-GENM: NVIDIA Generative AI Multimodal Reliable Test Blueprint

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NVIDIA Generative AI Multimodal Sample Questions (Q272-Q277):

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