NCA-GENM日本語版問題解説 & NCA-GENMテストサンプル問題

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信頼的なNCA-GENM日本語版問題解説一回合格-真実的なNCA-GENMテストサンプル問題

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NVIDIA Generative AI Multimodal 認定 NCA-GENM 試験問題 (Q312-Q317):

質問 # 312
You are working on a project that involves generating high-resolution images using a StyleGAN architecture. You observe that while the generated images are generally realistic, they often exhibit 'water droplet' artifacts. What could be a cause and solution to these artifacts?

A. A and C
B. The artifacts are likely due to aliasing during upsampling in the generator. Use filtered upsampling or anti-aliasing techniques to mitigate this.
C. The artifacts are a result of an unstable adversarial training process. Apply gradient penalty during training.
D. The artifacts are due to mode collapse. Use more diverse training data.
E. Increase the learning rate to avoid local minima.

正解：A

解説：
Water droplet artifacts often arise from aliasing in the upsampling layers of the generator network. Filtered upsampling (e.g., using a blur kernel before upsampling) or explicit anti-aliasing layers can smooth out the image and reduce these artifacts. Instabilities during adversarial training may amplify the artifacts. Gradient penalty helps stabilize the discriminator, thus improving the overall training stability and results.

質問 # 313
Which of the following is NOT a common challenge in training multimodal Generative AI models?

A. The computational complexity associated with training large unimodal models.
B. Handling different data modalities with varying statistical properties.
C. Dealing with missing modality data during inference.
D. Aligning feature spaces of different modalities.
E. Optimizing for a single modality at the expense of others.

正解：A

解説：
The computational complexity of training large unimodal models is a challenge for unimodal models, but not a distinct challenge inherent to multimodal models. Multimodal models have unique challenges related to data heterogeneity, feature alignment, handling missing modalities, and balancing performance across modalities.

質問 # 314
In a multimodal emotion recognition system, you are using both facial expressions and text messages as input. You observe that the model performs significantly better on individuals with clearly expressed facial emotions but poorly on individuals with subtle or masked facial expressions. Which of the following approaches would MOST directly address this bias?

A. Train a separate model specifically for individuals with subtle facial expressions.
B. Implement adversarial training to make the facial expression encoder invariant to expression intensity.
C. Increase the weight of the facial expression modality in the loss function.
D. Apply data augmentation techniques to the text message modality.
E. Collect more data from individuals with clearly expressed facial emotions.

正解：B

解説：
Adversarial training aims to make the model less sensitive to the intensity of the facial expression. By training an adversary to predict the expression intensity from the facial expression embeddings and then penalizing the main emotion recognition model for allowing the adversary to succeed, you force the facial expression encoder to focus on the underlying emotional content rather than the intensity of the expression. Training another Model for subtle emotions would be helpful, but adversarial training can tackle the model itself.

質問 # 315
Consider a multimodal dataset consisting of product reviews (text), product images, and customer demographics. You want to build a model that can predict customer satisfaction based on all three modalities. However, you suspect that there might be complex interactions between these modalities that are not easily captured by simple concatenation or averaging. What approach would be most effective for modeling these interactions?

A. Training separate models for each modality and then averaging their predictions.
B. Concatenating the feature vectors from each modality and feeding them into a single fully connected layer.
C. Using a tensor fusion network that explicitly models higher-order interactions between modalities.
D. Using a gated recurrent unit (GRU) to process the combined data.
E. Employing transfer learning, using pre-trained models for image and text processing then using the final combined layer for downstream task.

正解：C、E

解説：
Tensor fusion networks are designed to model complex, higher-order interactions between modalities. They create a tensor representation that captures all possible combinations of features from different modalities. This allows the model to learn intricate relationships that would be missed by simpler fusion techniques. Transfer learning is effective in scenarios where pre-trained models for image and text processing help boost the accuracy of final layer during downstream task.

質問 # 316
You're developing a multimodal system that takes an image and a short audio clip as input and generates a relevant story. You've trained the model, but you observe that the generated stories tend to heavily favor the content of the audio clip, largely ignoring the image. Which of the following techniques could you employ to better balance the influence of both modalities?

A. Add more layers to the audio encoder.
B. Increase the size of the training dataset, ensuring it contains more diverse audio clips.
C. Increase the learning rate of the image encoder.
D. Implement modality-specific scaling factors or attention mechanisms to dynamically adjust the contribution of each modality during the fusion process.
E. Reduce the dimensionality of the audio embedding_

正解：D

解説：
Modality-specific scaling factors and attention mechanisms provide a way to explicitly control the influence of each modality Increasing the learning rate of the image encoder might help, but it's not a direct solution- Reducing audio embedding dimensionality could reduce its influence, but might also lose important information. A more diverse dataset is always good, but doesn't guarantee balanced influence. Adding more layers to the audio encoder might increase its dominance.

質問 # 317
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