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NVIDIA Generative AI Multimodal Sample Questions (Q228-Q233):

NEW QUESTION # 228
You are working on a project to classify images of different types of flowers. You have a relatively small dataset (around 500 images per class). Which of the following techniques would be the MOST effective to improve the performance of your image classifier, considering the limited data?

A. Train a very deep convolutional neural network from scratch-
B. Use a pre-trained convolutional neural network on a large dataset like ImageNet and fine-tune it on your flower dataset
C. Reduce the image resolution to decrease the number of parameters in the model.
D. Apply aggressive data augmentation techniques, such as random rotations, flips, and crops.
E. Use a simple linear classifier.

Answer: B

Explanation:
Transfer learning, specifically fine-tuning a pre-trained model, is highly effective when dealing with small datasets. Pre-trained models have already learned useful features from large datasets, and fine-tuning them allows the model to adapt to the specific characteristics of your flower dataset. Training a deep network from scratch with limited data will likely lead to overfitting. Data augmentation helps, but transfer learning is generally more impactful. Reducing image resolution might lose important details, and a linear classifier might be too simple to capture the complexity of image features.

NEW QUESTION # 229
You are working with a pre-trained multimodal model that takes images and text as input. You want to fine-tune this model for a specific downstream task, but you have limited computational resources. Which of the following techniques would be most effective for reducing the memory footprint and computational cost during fine-tuning?

A. Applying knowledge distillation, where a smaller student model is trained to mimic the behavior of the pre-trained model.
B. Using quantization to reduce the precision of the model's weights and activations.
C. Freezing all layers of the pre-trained model and training only a small classification head.
D. Fine-tuning the entire model with a small learning rate.
E. Increasing the batch size to utilize the available memory more efficiently.

Answer: A,B

Explanation:
Quantization reduces the memory footprint of the model by using lower-precision representations for weights and activations. Knowledge distillation allows you to train a smaller, more efficient model that performs similarly to the larger pre-trained model. Freezing layers reduces the number of trainable parameters but may limit the model's ability to adapt to the new task. Fine-tuning the entire model, even with a small learning rate, is computationally expensive. Increasing batch size might lead to Out of Memory errors.

NEW QUESTION # 230
A multimodal A1 model is designed to translate sign language videos into text. The model performs well on videos with clear hand gestures and lighting conditions but struggles with videos recorded in low light or with partial hand occlusions. Which of the following strategies would be MOST effective in improving the model's robustness to these challenging conditions?

A. Reducing the frame rate of the input videos.
B. Increasing the size of the text vocabulary.
C. Using a simpler text encoder.
D. Applying image enhancement techniques (e.g., contrast adjustment, noise reduction) to the video frames.
E. Training the model on a smaller dataset.

Answer: D

Explanation:
Applying image enhancement techniques to the video frames can improve the visibility of hand gestures in low-light conditions and reduce the impact of noise, making the model more robust. Reducing the frame rate or training on a smaller dataset would likely decrease performance. Increasing the text vocabulary or using a simpler text encoder would not directly address the issue of poor video quality.

NEW QUESTION # 231
You are building a multimodal Generative AI system to generate marketing content. You have text descriptions of products, images of the products, and customer reviews. Which of the following strategies would best handle potential inconsistencies or contradictions between these different modalities?

A. Using a simple averaging method to combine the features from each modality.
B. Employing an attention mechanism or a cross-modal fusion network that learns to weigh the importance of each modality based on the context.
C. Prioritizing the image data as images are the most visually appealing and engaging.
D. Ignoring customer reviews as they are often unreliable.
E. Training separate models for each modality and then averaging the outputs.

Answer: B

Explanation:
Employing an attention mechanism or a cross-modal fusion network allows the model to dynamically learn which modalities are most relevant for a given input or generation task. This helps in resolving inconsistencies by giving more weight to reliable modalities and diminishing the influence of less reliable ones. Simple averaging or prioritization can lead to suboptimal results when modalities contradict each other.

NEW QUESTION # 232
You are experimenting with a text-to-image generative model. You notice that when prompted with descriptions containing specific demographic information (e.g., 'a black doctor'), the generated images consistently reflect stereotypes. What steps can you take during the experiment evaluation phase to identify and mitigate this bias? (Select TWO)

A. Randomly shuffle the training dataset to minimize bias.
B. Increase the size of the training dataset to dilute the effect of any biased examples.
C. Conduct a human evaluation study where participants assess the generated images for stereotypical representations.
D. Use a bias detection metric to quantify the presence of bias in the generated images, comparing output distributions across different demographic groups.
E. Filter out all examples containing demographic information from the training dataset.

Answer: C,D

Explanation:
Bias detection metrics (B) and human evaluation (D) are essential for identifying and quantifying bias in generated content. Increasing data size (A) alone might not solve the issue. Filtering demographic information (C) can lead to underrepresentation and unfair outcomes. Random shuffling (E) does not directly address inherent biases in the training data.

NEW QUESTION # 233
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Valid Test NCA-GENM Tutorial & Reliable NCA-GENM Dumps Book

Reliable NCA-GENM Dumps Book, Exam Sample NCA-GENM Online

NVIDIA Generative AI Multimodal Sample Questions (Q228-Q233):

NCA-GENM Learning Materials & NCA-GENM Study guide & NCA-GENM Reliable Dumps

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