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NVIDIA Generative AI Multimodal Sample Questions (Q124-Q129):

NEW QUESTION # 124
You are building a multimodal emotion recognition system that combines facial expressions (images) and spoken language (audio). The image data is preprocessed using a CNN, and the audio data is processed using an LSTM. Which of the following fusion strategies would be MOST effective for combining these two modalities to predict the emotion?

A. Training the CNN and LSTM models independently without any fusion.
B. Intermediate fusion by concatenating the CNN and LSTM hidden state representations before feeding them into a shared classification layer.
C. Late fusion by training separate classifiers on the CNN and LSTM outputs and then averaging their predicted probabilities.
D. Early fusion by concatenating the raw pixel values of the images with the raw audio waveform.
E. Using an attention mechanism to weigh the contributions of the CNN and LSTM features based on their relevance to the predicted emotion.

Answer: B,E

Explanation:
Early fusion is generally ineffective due to the high dimensionality and lack of meaningful relationships between raw pixel values and audio waveforms. Late fusion ignores the potential for interactions between modalities. Intermediate fusion allows the model to learn these interactions by combining representations at a higher level. Attention mechanism is even better because the model could weigh the contributions of the CNN and LSTM features.

NEW QUESTION # 125
In the context of generative models, what is the primary purpose of using a normalizing flow?

A. To improve the computational efficiency of backpropagation.
B. To introduce non-linearity into the encoder network of a VAE
C. To transform a simple probability distribution (e.g., Gaussian) into a more complex and flexible distribution that can better model the data.
D. To regularize the training of the discriminator in a GAN.
E. To reduce the dimensionality of the input data before feeding it to a generator network.

Answer: C

Explanation:
Normalizing flows are used to transform a simple probability distribution into a complex one by applying a series of invertible transformations. This allows the model to learn more complex data distributions and generate more realistic samples.

NEW QUESTION # 126
You are building a system that translates sign language videos into spoken text. You have a dataset of videos and corresponding text transcriptions. You notice that the test data contains significant variations in lighting conditions and camera angles compared to the training dat a. Which of the following techniques would be MOST effective in addressing this domain shift and improving the generalization of your model?

A. Use a domain adaptation technique such as Domain Adversarial Neural Networks (DANN) to learn domain-invariant features.
B. Only evaluate on a subset of the test data that closely resembles the training data.
C. Apply aggressive data augmentation techniques to the training data, including random crops, rotations, and color jittering to simulate the variations in the test data.
D. Reduce the size of the model to prevent overfitting to the training data.
E. Fine-tune the model on a small subset of the test data to adapt to the specific characteristics of the test distribution.

Answer: A

Explanation:
Domain adaptation techniques (C) are specifically designed to address domain shift by learning features that are invariant to the source and target domains. Data augmentation (A) can help but might not be sufficient. Fine-tuning on test data (B) is data leakage and invalidates the test set. Reducing model size (D) may not address the core issue of domain shift. Selecting a subset of the test data (E) defeats the purpose of testing generalization.

NEW QUESTION # 127
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. Fine-tune the entire model, including both text and image encoder layers, using a small learning rate.
B. Fine-tune only the image encoder layers, keeping the text encoder layers frozen.
C. Train a new classification head from scratch on top of the frozen pre-trained model.
D. Fine-tune the attention mechanism between the text and image encoders, while keeping the encoder weights frozen.
E. Fine-tune only the text encoder layers, keeping the image encoder layers frozen.

Answer: A

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 # 128
You observe that the generated images often lack fine-grained details and tend to be blurry. Which of the following techniques could MOST effectively improve the visual quality of the generated images?

A. Using a larger dataset of text-image pairs.
B. Increasing the batch size during training.
C. Decreasing the learning rate during training.
D. Implementing a discriminator network and using adversarial training (GAN).
E. Using a variational autoencoder (VAE) instead of a GAN.unlikely to significantly improve diagnosis accuracy.

Answer: D

Explanation:
Adversarial training (GANs) are known for generating sharper, more realistic images compared to other generative models. The discriminator encourages the generator to produce more realistic and detailed images to fool it. Increasing batch size (A) or using more data (B) can help, but GANs are specifically designed for image quality. Decreasing the learning rate (D) might stabilize training but doesn't directly address image sharpness. VAEs (E) tend to produce blurry images compared to GANs.

NEW QUESTION # 129
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Positive NCA-GENM Feedback | Test NCA-GENM Result

2025 Positive NCA-GENM Feedback: NVIDIA Generative AI Multimodal - High-quality NVIDIA Test NCA-GENM Result

NVIDIA Generative AI Multimodal Sample Questions (Q124-Q129):

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