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

1. You're using Stable Diffusion with a custom prompt to generate images of landscapes. You notice that the generated images consistently lack detail and appear blurry, despite increasing the number of inference steps. Which of the following prompt engineering techniques, combined with appropriate parameter tuning, is MOST likely to address this issue and improve the image's sharpness and detail?

A) Using completely unrelated keywords to encourage the model to create something unique.
B) Decreasing the 'guidance_scale' to allow for more creative freedom.
C) Using a very short and general prompt to allow the model more freedom.
D) Adding keywords like 'photorealistic', 'high resolution', '8k', 'detailed', and adjusting the 'clip_skip' parameter.
E) Specifying 'oil painting' or another artistic style to mask the lack of detail.

2. Consider the following Python code snippet utilizing the Hugging Face Transformers library for multimodal processing. The objective is to perform visual question answering (VQA). Assume 'image' is a PIL Image object and 'question' is a string. However, the code is incomplete. Choose the options to complete the code.

A)

B)

C)

D)

E)

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

4. You are working with a dataset of handwritten digits and training a Variational Autoencoder (VAE) to generate new digits. After training, you observe that the generated digits are blurry and lack sharp details. Which of the following modifications could potentially improve the quality of the generated digits in your VAE?

A) Decreasing the dimensionality of the latent space.
B) Reducing the weight of the KL divergence term in the VAE loss function.
C) Increasing the weight of the KL divergence term in the VAE loss function.
D) Increasing the capacity of the encoder and decoder networks (e.g., adding more layers or neurons).
E) Using a simpler decoder architecture.

5. You are tasked with generating realistic images of human faces using a GAN. However, you notice that the generated images often contain artifacts, such as distorted facial features or unrealistic textures. Which of the following techniques would be most effective in improving the realism and quality of the generated faces?

A) Employing a StyleGAN architecture with adaptive instance normalization (AdalN) and mapping network.
B) Training the GAN for fewer epochs.
C) Applying L1 regularization to the generator's weights.
D) Using a smaller batch size.
E) Using a simpler discriminator architecture.

問題與答案：