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質問 1：
You are building a real-time multimodal application that requires processing both audio and video streams simultaneously. You need to minimize the latency of the system while maximizing throughput. Which of the following hardware and software optimizations would be most effective?
A. Using a high-latency, high-bandwidth network connection.
B. Using separate GPUs for audio and video processing and employing asynchronous data transfer techniques.
C. Using a CPU-based implementation for both audio and video processing.
D. Compressing the audio and video streams aggressively to reduce the amount of data that needs to be processed.
E. Offloading both audio and video processing to a single high-end GPIJ.
正解：B
解説: (Pass4Test メンバーにのみ表示されます)
質問 2：
You're training a conditional GAN to generate images of birds based on text descriptions. The GAN generates images, but they lack fine- grained details and often have artifacts. Which of the following techniques are MOST likely to improve the quality and realism of the generated images? (Select TWO)
A. Using a simple Multi-Layer Perceptron (MLP) as the generator.
B. Using a more powerful discriminator architecture (e.g., with attention mechanisms).
C. Reducing the size of the input noise vector to the generator.
D. Implementing spectral normalization in both the generator and discriminator.
E. Using a deeper and wider generator network (e.g., with more layers and channels).
正解：D,E
解説: (Pass4Test メンバーにのみ表示されます)
質問 3：
You are developing a system to generate captions for videos. The video frames are processed using a pre-trained ResNet model, and the audio track is processed using a pre-trained Wav2Vec model. Which of the following techniques is MOST suitable for aligning the visual and audio features to generate accurate and coherent captions?
A. Using cross-attention mechanisms where the audio features attend to the visual features, and vice-versa, before feeding them into a Transformer decoder.
B. Training separate LSTMs for visual and audio features and averaging their outputs.
C. Using a simple feedforward network to combine the ResNet and Wav2Vec features.
D. Ignoring the audio track and only using the video frames.
E. Concatenating the ResNet and Wav2Vec features and feeding them into a single LSTM.
正解：A
解説: (Pass4Test メンバーにのみ表示されます)
質問 4：
You are experimenting with different loss functions for training a Variational Autoencoder (VAE) to generate images. You observe that using only the reconstruction loss (e.g., Mean Squared Error) results in blurry images. What other loss component is typically added to the VAE objective function to encourage the latent space to be well-structured and generate sharper images?
A. Cross-entropy loss
B. Hinge loss
C. Contrastive loss
D. Perceptual loss
E. Kullback-Leibler (KL) divergence loss
正解：E
解説: (Pass4Test メンバーにのみ表示されます)
質問 5：
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. Increase the size of the training dataset.
B. Increase the batch size.
C. Implement weight decay (L2 regularization).
D. Decrease the learning rate.
E. Add dropout layers to the model architecture.
正解：A,C,D,E
解説: (Pass4Test メンバーにのみ表示されます)
You are building a real-time multimodal application that requires processing both audio and video streams simultaneously. You need to minimize the latency of the system while maximizing throughput. Which of the following hardware and software optimizations would be most effective?
A. Using a high-latency, high-bandwidth network connection.
B. Using separate GPUs for audio and video processing and employing asynchronous data transfer techniques.
C. Using a CPU-based implementation for both audio and video processing.
D. Compressing the audio and video streams aggressively to reduce the amount of data that needs to be processed.
E. Offloading both audio and video processing to a single high-end GPIJ.
正解：B
解説: (Pass4Test メンバーにのみ表示されます)
質問 2：
You're training a conditional GAN to generate images of birds based on text descriptions. The GAN generates images, but they lack fine- grained details and often have artifacts. Which of the following techniques are MOST likely to improve the quality and realism of the generated images? (Select TWO)
A. Using a simple Multi-Layer Perceptron (MLP) as the generator.
B. Using a more powerful discriminator architecture (e.g., with attention mechanisms).
C. Reducing the size of the input noise vector to the generator.
D. Implementing spectral normalization in both the generator and discriminator.
E. Using a deeper and wider generator network (e.g., with more layers and channels).
正解：D,E
解説: (Pass4Test メンバーにのみ表示されます)
質問 3：
You are developing a system to generate captions for videos. The video frames are processed using a pre-trained ResNet model, and the audio track is processed using a pre-trained Wav2Vec model. Which of the following techniques is MOST suitable for aligning the visual and audio features to generate accurate and coherent captions?
A. Using cross-attention mechanisms where the audio features attend to the visual features, and vice-versa, before feeding them into a Transformer decoder.
B. Training separate LSTMs for visual and audio features and averaging their outputs.
C. Using a simple feedforward network to combine the ResNet and Wav2Vec features.
D. Ignoring the audio track and only using the video frames.
E. Concatenating the ResNet and Wav2Vec features and feeding them into a single LSTM.
正解：A
解説: (Pass4Test メンバーにのみ表示されます)
質問 4：
You are experimenting with different loss functions for training a Variational Autoencoder (VAE) to generate images. You observe that using only the reconstruction loss (e.g., Mean Squared Error) results in blurry images. What other loss component is typically added to the VAE objective function to encourage the latent space to be well-structured and generate sharper images?
A. Cross-entropy loss
B. Hinge loss
C. Contrastive loss
D. Perceptual loss
E. Kullback-Leibler (KL) divergence loss
正解：E
解説: (Pass4Test メンバーにのみ表示されます)
質問 5：
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. Increase the size of the training dataset.
B. Increase the batch size.
C. Implement weight decay (L2 regularization).
D. Decrease the learning rate.
E. Add dropout layers to the model architecture.
正解：A,C,D,E
解説: (Pass4Test メンバーにのみ表示されます)
藤沢** -
今日NCA-GENMの受験結果が出ました。高得点で合格になりました。有難いPass4Test本当に内容もすごく素晴らしかった。