NVIDIA-Certified Associate NCA-GENM

TestPDF 的 NVIDIA NCA-GENM題庫是由頂級IT專家團隊以最高技術水平整理製作的，確保了試題的準確性和專業性。這些IT團隊成員都是來自指定認證專家、培訓師和 NVIDIA 相關工作從業者，他們對 NCA-GENM考試內容和 NVIDIA-Certified Associate 認證要求的資歷瞭如指掌，這樣可以確保 NCA-GENM題庫的高質量。

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購買後，立即下載 NCA-GENM 題库 (NVIDIA Generative AI Multimodal): 成功付款後, 我們的體統將自動通過電子郵箱將你已購買的產品發送到你的郵箱。（如果在12小時內未收到，請聯繫我們，注意：不要忘記檢查你的垃圾郵件。）

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

1. You are tasked with building a system that can answer questions based on both an image and a corresponding text description. The image is represented as a feature vector from a CNN, and the text is represented as a sequence of word embeddings from a pre-trained language model. Which architecture would be most suitable for this task?

A) Two separate models, one for processing images and another for processing text, with the final answer being chosen based on the higher confidence score.
B) A combination of a CNN and an LSTM, where the CNN processes the image and the LSTM processes the text independently.
C) A recurrent neural network (RNN) that processes the text and then uses the final hidden state to attend to the image features.
D) A simple feedforward neural network that concatenates the image and text feature vectors.
E) A Transformer-based architecture with cross-attention mechanisms that allow the model to attend to both the image and text features simultaneously.

2. Which of the following techniques are MOST effective for improving the energy efficiency of a large-scale Generative A1 model during inference, while minimizing performance degradation?

A) Knowledge distillation to a smaller model
B) Gradient accumulation
C) Pruning (removing less important weights)
D) Increasing the batch size significantly
E) Model quantization (e.g., INT8)

3. You're fine-tuning a pre-trained multimodal model for a specific downstream task. You notice that while the model's performance on the training data is excellent, it performs poorly on unseen dat a. What regularization technique, beyond standard weight decay, is MOST likely to improve the model's generalization ability in this scenario, and what is its purpose?

A) Early Stopping: To halt training when performance on a validation set degrades.
B) Layer Normalization: To normalize activations across features, stabilizing training.
C) Gradient Clipping: To prevent exploding gradients, stabilizing training.
D) Dropout: To randomly deactivate neurons during training, preventing co-adaptation and improving robustness.
E) Batch Normalization: To accelerate training and reduce internal covariate shift.

4. You are building a multimodal RAG application that integrates text documents and images. You've noticed that when a user query relates strongly to the visual content, the retrieved documents are less relevant than desired. Which of the following strategies would MOST effectively improve the retrieval of relevant information in this scenario?

A) Use a larger language model for the generative component of the RAG pipeline.
B) Fine-tune the existing text embedding model with more text data.
C) Implement cross-modal embedding, training a model to create a joint embedding space for text and images.
D) Increase the k value (number of retrieved documents) in the vector search.
E) Prepend the image captions to all the source documents to enhance text-based retrieval.

5. You are building a multimodal generative A1 system that creates 3D models from text descriptions. The system produces accurate shapes but struggles to generate realistic textures and surface details. What approach would BEST address this limitation?

A) Reduce the resolution of the generated 3D models to simplify the texture generation process.
B) Train a separate texture generation network conditioned on the generated 3D shape.
C) Increase the batch size during the 3D model generation phase.
D) Increase the number of parameters in the text encoder.
E) Add more layers to the shape decoder.

問題與答案：