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NVIDIA Generative AI Multimodal Sample Questions (Q255-Q260):

NEW QUESTION # 255
You're training a large language model (LLM) and notice that it struggles to maintain consistency and context over long passages of text. Which of the following architectural modifications would be most effective in addressing this issue?

A. Using a smaller embedding dimension.
B. Reducing the number of layers in the transformer architecture.
C. Increasing the maximum sequence length the model can process.
D. Implementing a sparse attention mechanism to reduce computational cost
E. Increasing the size of the vocabulary.

Answer: C

Explanation:
Increasing the maximum sequence length allows the model to consider a larger context window when generating text, improving its ability to maintain consistency and coherence over longer passages. Other options might address other issues, but increasing sequence length directly tackles the problem of limited context.

NEW QUESTION # 256
Which of the following is NOT a typical application or benefit of using U-Net architectures in generative AI, particularly within the context of image generation and manipulation?

A. Encoding high-dimensional text data for multimodal embeddings.
B. Medical image analysis, such as tumor detection.
C. Image segmentation and pixel-wise classification.
D. Image inpainting and super-resolution tasks.
E. Facilitating efficient feature extraction and upsampling for detailed image generation.

Answer: A

Explanation:
U-Nets are primarily used for image-to-image tasks like segmentation, inpainting, and super-resolution. They excel at processing and generating images, but are not directly involved in encoding text data. CLIP is used for that purpose.

NEW QUESTION # 257
You're building a virtual assistant using NVIDIAAvatar Cloud Engine (ACE). You want the avatar to respond to user queries with realistic facial expressions and lip synchronization. Which ACE components are essential for achieving this?

A. Riva ASR, Riva TTS, and Audi02Emotion.
B. Riva ASR, Riva TTS, Audi02Emotion, and a 3D avatar model.
C. Only a 3D avatar model.
D. Riva ASR, Riva TTS, Audi02Emotion, a 3D avatar model, and an animation engine.
E. only Riva ASR and TTS.

Answer: D

Explanation:
A complete ACE setup for realistic avatar interaction requires: Automatic Speech Recognition (ASR) to understand the user's query, Text-to-Speech (TTS) to generate the avatar's response, Audi02Emotion to infer emotional expressions from the text/audio, a 3D avatar model to represent the avatar visually, and an animation engine to drive facial expressions and lip synchronization. This combination ensures a lifelike and engaging user experience.

NEW QUESTION # 258
You are working on a multimodal A1 model that translates spoken language from one language (e.g., English) to another (e.g., Spanish) while also generating a corresponding visual representation of the translated sentence. You have access to a large dataset of parallel spoken language and image pairs, but the image quality is highly variable. Some images are clear and detailed, while others are blurry and noisy. How should you best handle the data to build the multimodal system?

A. Ignore image data entirely and train only with spoken language.
B. Implement a curriculum learning approach, starting with high-quality images and gradually introducing low-quality images as the model improves.
C. Use a pre-trained image enhancement model to improve the quality of the low-quality images before training the multimodal model.
D. Train the model using all images, but apply a higher weight to the loss function for high-quality images.
E. Train the model solely on the high-quality images, discarding the low-quality images.

Answer: B,C

Explanation:
Using a pre-trained image enhancement model (C) attempts to clean up the noisy images. Training using curriculum learning approach (D) is often suitable for the low-quality images. Simply discarding data would reduce the size of your dataset which can hurt performance. Applying high weight to loss function (B) and training with only high quality images (A) isn't sufficient. Ignoring data will reduce the number of modalities.

NEW QUESTION # 259
You are analyzing a dataset of customer reviews for a Generative A1-powered product. You want to identify the key themes and topics that customers are discussing. Which technique would be MOST appropriate for this task?

A. Regression analysis to predict customer satisfaction scores.
B. Sentiment analysis to determine the overall positive or negative sentiment.
C. Time series analysis to track review volume over time.
D. Topic modeling (e.g., LDA, NMF) to discover underlying themes in the reviews.
E. Clustering analysis to group customers based on demographics.

Answer: D

Explanation:
Topic modeling is specifically designed to discover the underlying themes and topics within a collection of documents (in this case, customer reviews). LDA (Latent Dirichlet Allocation) and NMF (Non-negative Matrix Factorization) are common topic modeling techniques. Sentiment analysis provides an overall sentiment score but doesn't identify specific themes. Regression analysis is for prediction, not theme discovery. Clustering groups customers, not review topics. Time series analysis tracks changes over time, not themes.

NEW QUESTION # 260
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