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NVIDIA Generative AI Multimodal Sample Questions:

1. You have developed a multimodal model that uses both audio and video data to detect human emotions. During testing, you observe that the model performs exceptionally well on controlled lab recordings but poorly in real-world scenarios with background noise and varying lighting conditions. What technique would be MOST effective in improving the model's generalization ability to real-world data?

A) Replacing the audio input with text transcripts.
B) Data augmentation techniques such as adding noise to the audio, simulating different lighting conditions for the video, and using transfer learning from pre- trained audio and video models.
C) Increasing the amount of data from lab recordings.
D) Training separate models for lab recordings and real-world data.
E) Reducing the model's complexity to prevent overfitting to the lab recordings.

2. Which of the following techniques can be used to improve the factual accuracy of text generated by a large language model?

A) Always using the same prompt, regardless of the desired output.
B) Applying a temperature of 0 during text generation.
C) Fine-tuning the model on a dataset of factually correct information.
D) Using retrieval-augmented generation (RAG) to ground the model's knowledge in external sources.
E) Increasing the model size and training it on more data.

3. 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) Using a rule-based system for response generation, avoiding the complexity of training and deploying an LLM.
B) 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.
C) Primarily focusing on high-fidelity avatar rendering using Omniverse, neglecting the accuracy of speech recognition and the quality of the generated responses.
D) 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.
E) Using a small, low-latency speech recognition model to ensure quick response times, even if it results in reduced accuracy and frequent errors.

4. You're building a multimodal model that predicts customer satisfaction based on their written reviews and associated call center audio recordings. You've pre-trained separate text and audio encoders. What's the MOST effective strategy to fuse these modalities for the final prediction task?

A) Fine-tune only the text encoder, keeping the audio encoder frozen.
B) Add the hidden states of both encoders element-wise.
C) Average the output embeddings of both encoders element-wise.
D) Train a separate attention mechanism to weigh the contributions of each modality before concatenation.
E) Concatenate the final hidden states from both encoders and feed them into a fully connected layer.

5. You are using NeMo to fine-tune a large language model for a specific task. You notice that the model is overfitting to the training dat a. Which of the following techniques could you apply to mitigate overfitting in this scenario? (Select all that apply)

A) Decrease the learning rate.
B) Increase the size of the training dataset.
C) Increase the batch size.
D) Add dropout layers to the model architecture.
E) Implement weight decay (L2 regularization).

Solutions: