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NVIDIA Generative AI Multimodal Sample Questions (Q295-Q300):

NEW QUESTION # 295
You're building an application utilizing NVIDIA ACE to create interactive virtual assistants. The goal is to have the assistant respond to user queries in a natural and contextually relevant way. Which of the following choices, when implemented together, would significantly contribute to achieving this objective?

A. Primarily focusing on high-fidelity avatar rendering using Omniverse, neglecting the accuracy of speech recognition and the quality of the generated responses.
B. Using a rule-based system for response generation, avoiding the complexity of training and deploying an LLM.
C. Relying solely on pre-trained LLMs without fine-tuning for the specific application domain, assuming that the models will generalize well to all types of user queries.
D. Using a small, low-latency speech recognition model to ensure quick response times, even if it results in reduced accuracy and frequent errors.
E. Employing Riva for accurate Automatic Speech Recognition (ASR) and Text-to-Speech (TTS), utilizing a large language model (LLM) fine-tuned on a domain-specific dataset for natural language understanding and response generation, and integrating a dialogue management system to maintain conversation context and manage turn-taking.

Answer: E

Explanation:
Natural and contextually relevant responses require accurate speech recognition, a capable LLM, and a dialogue management system to track context. Riva provides the speech components. A domain-specific LLM improves accuracy. Dialogue management enables coherent conversations. High-fidelity rendering (B) is secondary. Pre-trained LLMs (C) might lack domain expertise. Rule-based systems (D) are inflexible. Sacrificing accuracy for latency (E) hurts the user experience.

NEW QUESTION # 296
You're building a multimodal model to generate captions for videos. You've noticed that your model struggles to capture temporal relationships and sequential dependencies in the video frames. Which of the following architectures or techniques would be BEST suited to address this?

A. A standard Convolutional Neural Network (CNN) applied independently to each frame.
B. A combination of a CNN to extract features from individual frames, followed by a Recurrent Neural Network (RNN) like LSTM or GRU to model temporal dependencies between the extracted features.
C. A Multilayer Perceptron (MLP) trained on flattened video frames.
D. A 3D Convolutional Neural Network (3D CNN) that processes multiple frames as a volume.
E. Principal Component Analysis (PCA) to reduce dimensionality of each frame before feeding to the decoder

Answer: B

Explanation:
RNNs, specifically LSTMs and GRUs, are designed to handle sequential data and capture temporal relationships. Combining a CNN for feature extraction with an RNN allows the model to process individual frames and then model the dependencies between them. 3D CNNs can also capture temporal information, but can be computationally expensive. Other options don't explicitly address the temporal aspect.

NEW QUESTION # 297
You are training a multimodal generative A1 model that takes text and images as input to generate videos. During experimentation, you observe that the model performs well on common scenarios (e.g., 'a dog playing in the park') but struggles to generate coherent videos for less frequent or abstract scenarios (e.g., 'the concept of time flowing'). What is the MOST effective strategy to improve the model's performance on these challenging scenarios, focusing on test data quality?

A. Increase the size of the training dataset by duplicating existing common scenario examples.
B. Reduce the complexity of the model architecture to prevent overfitting on the common scenarios.
C. Implement data augmentation techniques on the existing training data, focusing on color adjustments and minor image transformations.
D. Train the model for a significantly longer duration on the existing training data.
E. Curate a new test dataset specifically containing challenging scenarios and use it to evaluate and fine-tune the model. Ensure the new test data includes diverse interpretations and variations of the abstract concepts.

Answer: E

Explanation:
Curating a specific test dataset for challenging scenarios allows for targeted evaluation and fine-tuning. Duplicating common scenarios (A) won't address the problem. Simple augmentations (B) may not be sufficient. Reducing model complexity (D) might hurt overall performance. Longer training (E) without addressing data bias is unlikely to help significantly.

NEW QUESTION # 298
You are fine-tuning a pre-trained large language model (LLM) for a specific text generation task using LoRA (Low-Rank Adaptation).
Which of the following statements accurately describes the benefits and limitations of using LoRA?

A. LoRA is not compatible with model parallelism techniques.
B. LoRA can improve the accuracy of the fine-tuned model compared to full fine-tuning by preventing overfitting.
C. LoRA reduces the number of trainable parameters by inserting low-rank matrices into the original model layers, making fine-tuning more memory-efficient.
D. A and B.
E. LoRA allows for efficient task switching by only storing and loading the small LoRA parameters for different tasks, while keeping the original LLM weights frozen.

Answer: D

Explanation:
LoRA significantly reduces the number of trainable parameters, enabling more memory-efficient fine-tuning, especially for large models. It also facilitates efficient task switching as only the small LoRA parameters need to be stored and loaded for different tasks. While LoRA can help prevent overfitting compared to full fine-tuning, it doesn't guarantee improved accuracy. LoRA can be effectively combined with model parallelism.

NEW QUESTION # 299
A research team is developing a multimodal model to predict stock prices using financial news articles, company filings (text), historical stock prices (time-series), and executive interviews (audio). They are experiencing significant performance issues due to inconsistent data quality across modalities. What specific strategies would you recommend to address these data quality challenges?

A. Focus exclusively on improving the quality of the most readily available data source.
B. Implement audio transcription and sentiment analysis on executive interviews to extract key information and emotional tone.
C. Normalize and scale historical stock prices to a consistent range to avoid dominance by high-magnitude values.
D. All of the above.
E. Apply Named Entity Recognition (NER) to financial news and company filings to standardize company names and financial terms.

Answer: D

Explanation:
All the options are essential. NER standardizes textual data, audio analysis extracts sentiment, and normalization prevents stock price dominance. Addressing data quality holistically across modalities is key.

NEW QUESTION # 300
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