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NVIDIA Generative AI Multimodal Sample Questions (Q61-Q66):

NEW QUESTION # 61
You're working on a multimodal A1 model that combines audio and text to generate music. You notice that the generated music lacks musical structure and sounds random. Which of the following techniques could be applied to improve the coherence and musicality of the generated output?

A. Using a Variational Autoencoder (VAE) to learn a latent representation of musical structure.
B. Adding more layers to the model.
C. Training the model on a larger dataset of music.
D. Using a Recurrent Neural Network (RNN) with attention mechanism to model sequential dependencies in the music.
E. Increasing the size of the model's hidden layers.

Answer: A,D

Explanation:
A VAE can learn a structured latent space that captures essential musical features, allowing for controlled generation. RNNs with attention are well-suited for modeling sequential data like music, capturing long-range dependencies and creating a more coherent structure. Simply increasing the size or depth of the model may not address the underlying issue of musical structure. A larger dataset may help, but structured modeling techniques are generally more effective.

NEW QUESTION # 62
You are building a multimodal application that needs to understand both image and text dat a. You want to use a pre-trained model but fine-tune it for your specific task. Which of the following strategies is MOST effective for fine-tuning a large pre-trained multimodal model?

A. Train a new classification head from scratch on top of the frozen pre-trained model.
B. Fine-tune the attention mechanism between the text and image encoders, while keeping the encoder weights frozen.
C. Fine-tune only the image encoder layers, keeping the text encoder layers frozen.
D. Fine-tune the entire model, including both text and image encoder layers, using a small learning rate.
E. Fine-tune only the text encoder layers, keeping the image encoder layers frozen.

Answer: D

Explanation:
Fine-tuning the entire model with a small learning rate allows the model to adapt to the specific nuances of the new task while leveraging the knowledge already learned during pre-training. Freezing layers can limit adaptability. Training only a new head might not fully utilize the pre-trained features.

NEW QUESTION # 63
You're developing a multimodal system that takes an image and a short audio clip as input and generates a relevant story. You've trained the model, but you observe that the generated stories tend to heavily favor the content of the audio clip, largely ignoring the image. Which of the following techniques could you employ to better balance the influence of both modalities?

A. Reduce the dimensionality of the audio embedding_
B. Increase the learning rate of the image encoder.
C. Implement modality-specific scaling factors or attention mechanisms to dynamically adjust the contribution of each modality during the fusion process.
D. Add more layers to the audio encoder.
E. Increase the size of the training dataset, ensuring it contains more diverse audio clips.

Answer: C

Explanation:
Modality-specific scaling factors and attention mechanisms provide a way to explicitly control the influence of each modality Increasing the learning rate of the image encoder might help, but it's not a direct solution- Reducing audio embedding dimensionality could reduce its influence, but might also lose important information. A more diverse dataset is always good, but doesn't guarantee balanced influence. Adding more layers to the audio encoder might increase its dominance.

NEW QUESTION # 64
Consider the following PyTorch code snippet used for training a Generative A1 model:

A. The code is correct and will train the model efficiently.
B. The learning rate scheduler is not being used correctly.
C. The model parameters will not be updated correctly since optimizer.step() is called outside the loop.
D. CUDAOOM error because gradients are accumulating without updating parameters.
E. The code will run, but it's computationally inefficient. Gradients should be zeroed before each backward pass.

Answer: C,D

Explanation:

The code has two critical issues. First, 'optimizer.step()' is called only once per epoch after accumulating gradients from all batches. This is incorrect, as parameters aren't updated batch-wise. Second, is also called only once per epoch, meaning gradients from all batches accumulate. This will likely lead to a CUDAOOM error, especially for larger models.

NEW QUESTION # 65
You are developing a system to generate captions for videos. The video frames are processed using a pre-trained ResNet model, and the audio track is processed using a pre-trained Wav2Vec model. Which of the following techniques is MOST suitable for aligning the visual and audio features to generate accurate and coherent captions?

A. Concatenating the ResNet and Wav2Vec features and feeding them into a single LSTM.
B. Training separate LSTMs for visual and audio features and averaging their outputs.
C. Using a simple feedforward network to combine the ResNet and Wav2Vec features.
D. Using cross-attention mechanisms where the audio features attend to the visual features, and vice-versa, before feeding them into a Transformer decoder.
E. Ignoring the audio track and only using the video frames.

Answer: D

Explanation:
Cross-attention allows the model to learn the temporal relationships and dependencies between the visual and audio modalities. The audio features can attend to relevant visual features at each time step, and vice versa, leading to better alignment and more coherent captions. Simple concatenation and averaging are less effective at capturing these complex relationships. Ignoring the audio track loses valuable information.

NEW QUESTION # 66
......

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