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Free PDF Quiz 2025 NVIDIA Newest NCA-GENM: NVIDIA Generative AI Multimodal Test Tutorials

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NVIDIA Generative AI Multimodal Sample Questions (Q191-Q196):

NEW QUESTION # 191
You are building a multimodal emotion recognition system that takes both facial expressions (images) and speech audio as input. During development, you observe that the model is heavily biased towards the audio modality, effectively ignoring the visual input. Which technique would be the LEAST effective in mitigating this modality bias?

A. Adversarial training to make each modality indistinguishable.
B. Increasing the complexity of the audio processing branch and simplifying the image processing branch of the model.
C. Reweighting the loss function to penalize errors made based on the less dominant modality (image).
D. Gradient blending: Adjusting the gradients from each modality based on their relative importance.
E. Modality dropout: Randomly dropping out one of the modalities during training.

Answer: B

Explanation:
Increasing the audio branch's complexity while simplifying the image branch would actually exacerbate the modality bias towards audio. The other techniques (modality dropout, gradient blending, loss reweighting, and adversarial training) are all strategies designed to encourage the model to utilize both modalities more evenly. Increasing the model parameters of one mode leads to over-representation of that mode.

NEW QUESTION # 192
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).

Answer: A,B,D,E

Explanation:
Overfitting occurs when a model learns the training data too well and performs poorly on unseen data. Increasing the size of the training dataset provides the model with more diverse examples. Decreasing the learning rate helps the model converge to a more generalizable solution. Weight decay penalizes large weights, preventing the model from becoming too specialized to the training data. Dropout randomly disables neurons during training, forcing the model to learn more robust features. Increasing batch size can sometime prevent model to converge You are developing a system that uses a generative AI model to create personalized avatars for users based on their descriptions.

NEW QUESTION # 193
You are tasked with creating a multimodal A1 assistant that can understand and respond to user queries based on images and text. The assistant should be able to identify objects in images, understand the relationships between them, and answer questions about the image content using natural language. Given a scenario where a user uploads an image of a living room and asks, 'What is the color of the sofa next to the window?', what are the essential steps and techniques needed to implement this functionality?

A. Sentiment Analysis.
B. Relationship extraction: Use a relationship extraction model to determine the spatial relationships between the detected objects (e.g., 'sofa is next to window').
C. All of the above.
D. Object detection: Use an object detection model (e.g., YOLO, Faster R-CNN) to identify objects in the image (sofa, window, etc.).
E. Visual question answering (VQA): Use a VQA model that takes the image and the user's question as input and generates a natural language answer (e.g., 'The sofa is blue').

Answer: C

Explanation:
All steps are required. Object detection identifies the objects, relationship extraction understands their spatial relationships, and VQA generates the natural language answer based on the image and question. Sentiment analysis is not relevant in this scenario.

NEW QUESTION # 194
You want to ensure that the system respects user privacy and avoids generating avatars that resemble real people without their consent.
Which of the following strategies would be MOST effective in addressing this ethical concern?

A. Using a diverse and anonymized dataset for training and adding a mechanism to prevent the model from generating highly realistic facial features.
B. Implementing a face recognition system to detect and filter out avatars that closely resemble existing individuals.
C. Training the model on a dataset that includes images of real people with explicit consent.
D. Adding watermarks to all generated avatars to indicate that they are A1-generated.to global minimum.
E. Generating all avatars with a cartoonish or stylized appearance to avoid any resemblance to real people.

Answer: A

Explanation:
Using a diverse and anonymized dataset reduces the risk of the model learning to generate avatars that are too similar to specific individuals. Preventing the generation of highly realistic facial features further mitigates this risk while still allowing for personalized avatars. Ethical concerns are paramount when developing generative A1 systems, particularly when dealing with potentially sensitive data such as facial features.

NEW QUESTION # 195
Consider a scenario where you are evaluating the performance of a multimodal A1 model that generates descriptions for images. However, the generated descriptions tend to be repetitive and lack diversity. Which of the following techniques can be employed to address this issue and encourage more diverse and creative outputs from the model? (Select TWO)

A. Utilizing a temperature scaling parameter during decoding and increasing its value.
B. Employing a nucleus sampling (top-p sampling) decoding strategy.
C. Increasing the model's training data size.
D. Using a greedy decoding strategy.
E. Using a beam search decoding strategy with a small beam width.

Answer: A,B

Explanation:
Nucleus sampling (top-p sampling) randomly samples from the smallest set of words whose cumulative probability mass exceeds a threshold p, encouraging more diverse outputs. Increasing the temperature scaling parameter makes the probability distribution flatter, leading to more exploration and less predictable (more creative) outputs. Beam search with a small beam width might still result in repetitive outputs. Increasing the training data size can help, but it might not directly address the lack of diversity. Greedy decoding always selects the most probable word, leading to repetitive and predictable outputs.

NEW QUESTION # 196
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