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最新的 NVIDIA-Certified Associate NCA-GENM 免費考試真題:

1. You are working on a project to generate realistic images from text descriptions. You've trained a diffusion model, but the generated images often lack fine-grained details and exhibit artifacts. Which of the following techniques would be MOST effective in improving the image quality and fidelity?

A) None of the above.
B) Increase the number of diffusion steps during image generation.
C) Use a larger batch size during training.
D) Implement classifier-free guidance, adjusting the guidance scale to balance fidelity and diversity.
E) Reduce the learning rate during training.

2. You are tasked with analyzing a large dataset of images used for training a generative A1 model. The dataset contains noisy labels and varying image quality. Which of the following preprocessing steps are MOST crucial for improving the performance of your model?

A) Converting all images to grayscale to reduce computational complexity.
B) Using a pre-trained image quality assessment model to filter out low-quality images.
C) Implementing a label smoothing technique to mitigate the impact of noisy labels.
D) Resizing all images to a fixed resolution (e.g., 256x256).
E) Applying aggressive data augmentation techniques like random rotations and flips.

3. Consider a multimodal dataset containing patient records: text descriptions of symptoms, MRI images, and audio recordings of heart sounds. Some records are missing MRI images. Which of the following methods is BEST suited for handling this missing data within a multimodal learning framework?

A) Using a masking approach during training, where the model is trained to predict the missing modality (MRI) from the available modalities (text and audio) for incomplete records and is trained with all modalities for complete records.
B) Ignoring the MRI data completely and training the model only on the text and audio data.
C) Deleting all records with missing MRI images.
D) Training a separate model only on records with complete data and then using it to predict the missing data.
E) Imputing missing MRI images using the average MRI image from the entire dataset.

4. You are developing a multimodal AI model that processes both text and images to classify news articles as either 'reliable' or 'unreliable'. After training, you notice that the model performs well on articles with strong visual cues (e.g., professionally edited images), but struggles with articles that have only text or low-quality images. Which of the following techniques would be MOST effective in improving the model's robustness and generalizability across different types of news articles?

A) Reduce the weight of the image modality in the overall loss function.
B) Replace the image processing component with a simpler, less powerful model.
C) Exclusively train the model on articles with high-quality images to improve its visual processing capabilities.
D) Increase the size of the training dataset by only adding more data with high quality images.
E) Implement a modality dropout strategy during training, randomly masking either the text or image input to force the model to rely more on the available modality.

5. Which of the following statements accurately describes the role of attention mechanisms in Transformer-based multimodal models?
(Select all that apply)

A) Attention mechanisms are used to compress the input sequence into a fixed-length vector representation.
B) Attention mechanisms enable the model to learn relationships between different modalities, such as images and text.
C) Attention mechanisms allow the model to focus on the most relevant parts of the input sequence when generating the output.
D) Attention mechanisms are primarily used to reduce the computational cost of processing long sequences.
E) Attention mechanisms prevent vanishing gradients during training of deep neural networks.

問題與答案：