NCA-GENM試験無料問題集「NVIDIA Generative AI Multimodal 認定」

A. LoRA is a data augmentation technique used to increase the size of the training dataset.

B. LoRA is a regularization technique used to prevent overfitting during fine-tuning.

C. LoRA is a fine-tuning technique that freezes the original weights of a pre-trained model and trains a small set of low-rank matrices to adapt the model to a specific task.

D. LoRA is a method for compressing the weights of a pre-trained language model to reduce its memory footprint.

E. LoRA is a type of attention mechanism used in transformer models.

A. Adding more layers to the model to increase its representational capacity.

B. Pruning to remove less important connections in the model.

C. Using larger batch sizes during inference to maximize GPIJ utilization.

D. Increasing the number of attention heads in the transformer architecture.

E. Model quantization (e.g., INT8) to reduce model size and improve inference speed.

A. 

B. 

C. 

D. 

E. 

A. Converting the model to a smaller architecture with fewer parameters, accepting a lower accuracy.

B. Offloading complex computations to a cloud server.

C. Using NVIDIA TensorRT to optimize the model for the specific edge device.

D. Using a larger batch size to improve GPU utilization.

E. Implementing distributed inference across multiple edge devices.

A. Adding random noise to the audio track.

B. Applying random rotations, flips, and color jittering to the video frames.

C. Randomly cropping and scaling the video frames.

D. Replacing random words in the text descriptions with synonyms.

E. Reducing the frame rate of the videos.

A. The 'style_image' is not preprocessed correctly before being passed to the 'refine_image' function.

B. The 'generate_image' function does not support the parameter.

C. The 'strength' parameter in 'refine_image' is set too low, resulting in minimal stylistic changes.

D. The library being used is incompatible with the GPU.

E. The text prompt provided is too short.

A. Apply aggressive data augmentation techniques, such as random rotations, flips, and crops.

B. Reduce the image resolution to decrease the number of parameters in the model.

C. Use a simple linear classifier.

D. Train a very deep convolutional neural network from scratch-

E. Use a pre-trained convolutional neural network on a large dataset like ImageNet and fine-tune it on your flower dataset

A. Increasing the batch size during inference to better utilize GPU resources.

B. Using a simpler loss function during training.

C. Switching to a larger model architecture with more parameters.

D. Implementing TensorRT for model optimization and quantization.

E. Removing dropout layers from the model.

A. Increase the number of Transformer layers to improve caption accuracy, even if it increases latency.

B. Implement knowledge distillation, training a smaller, faster student model to mimic the behavior of the larger Transformer model.

C. Employ model quantization techniques (e.g., INT8 quantization) to reduce model size and memory bandwidth requirements.

D. Use mixed precision training and inference, leveraging Tensor Cores on NVIDIA GPUs.

E. Decrease the batch size during inference.

A. Use a separate data processing pipeline outside of NeMo and save the augmented data to disk before training.

B. Monkey-patch the existing NeMo data loading functions to inject your augmentation logic.

C. Augment the data directly within the training loop, applying transformations to each batch before feeding it to the model. method.

D. Create a custom *Dataset* class that inherits from 'nemo.core.Dataset' and implements your augmentation within the '_getitem

E. Modify the core NeMo library files to directly incorporate your augmentation logic.

A. Natural Language Understanding (NLU)

B. Text to Speech (TTS)

C. speech to Text (STT)

D. Facial Animation

E. Lip Sync

A. torch.Size([256, 512]). This implementation is correct and efficient for cross-modal attention.

B. torch.Size ([32, 32]). This is correct and computes attention weights for each text-image pair in the batch independently

C. torch.Size ([32, 5121). The issue is a dimension mismatch.

D. Error. The transpose operation is incorrect for achieving cross-modal attention.

E. 

A. Apply data augmentation techniques specifically designed for audio, such as adding noise or varying the speed and pitch.

B. Increase the learning rate for the audio modality during training.

C. Use transfer learning by pre-training the audio component of the model on a large audio dataset.

D. Decrease the weight of the audio modality in the loss function.

E. Reduce the dimensionality of the audio features to simplify the learning task.

A. Guaranteed perfect accuracy.

B. Improved robustness to noise and missing data in one modality.

C. Enhanced ability to capture complex relationships between different data types.

D. Simpler model architectures.

E. Reduced computational complexity.