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

1. You are integrating a generative A1 model into a client's existing software infrastructure. The client is concerned about data privacy and security. What steps should you take during data gathering, deployment, and integration to address these concerns, while also using NVIDIA tools effectively?
Select all that apply:

A) Implement federated learning, training the generative A1 model on the client's data in a distributed manner without directly accessing or transferring the raw data. Use NVIDIA FLARE for orchestrating the federated learning process.
B) Avoid using any client data for training the generative A1 model, instead relying on publicly available datasets to minimize privacy risks.
C) Implement differential privacy techniques during data collection and model training to protect sensitive information. Leverage NVIDIA's Merlin framework for privacy-preserving data preprocessing.
D) Only utilize pre-trained open-source models
E) Deploy the generative A1 model on-premises within the client's secure network, using Triton Inference Server to ensure controlled access and prevent data leakage.

2. Consider the following Python code snippet used for processing image and text data for a multimodal model:

What is the primary limitation of the text encoding method used in this code, and how could it be improved for use in a real-world multimodal model?

A) The text encoding is efficient but incompatible with common deep learning architectures.
B) The text encoding is overly complex and should be simplified to reduce computational overhead.
C) It adequately addresses the complexities inherent in natural language, making it suitable for a variety of multimodal models.
D) The text encoding is suitable for small datasets but will not scale to larger datasets.
E) The text encoding only supports ASCII characters and does not account for word embeddings or sequence length variations. Use a tokenizer like BERT or SentencePiece to generate embeddings and pad sequences to a fixed length

3. You're developing a multimodal A1 system that takes image data, text descriptions, and user interaction data (clicks, dwell time) to generate personalized product recommendations. To effectively combine these modalities and capture complex relationships, which model architecture would be most suitable?

A) A decision tree-based model.
B) A k-nearest neighbors (KNN) algorithm.
C) A deep learning architecture incorporating attention mechanisms and cross-modal fusion layers, with separate embedding layers for each modality, followed by a shared representation layer for joint learning and prediction.
D) A simple linear regression model.
E) A Naive Bayes classifier.

4. Which of the following statements accurately describes the purpose and functionality of 'LoRA' (Low-Rank Adaptation) in the context of fine-tuning large language models?

A) LoRA is a data augmentation technique used to increase the size of the training dataset.
B) 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.
C) LoRA is a method for compressing the weights of a pre-trained language model to reduce its memory footprint.
D) LoRA is a regularization technique used to prevent overfitting during fine-tuning.
E) LoRA is a type of attention mechanism used in transformer models.

5. You have trained a multimodal model for visual question answering (VQA). During inference, the model often generates incorrect answers even though it seems to understand the question and the image content. Which of the following strategies could help improve the accuracy of the model's predictions? (Select all that apply)

A) Reduce the size of the training dataset.
B) Use beam search decoding to explore multiple possible answer sequences.
C) Increase the learning rate during fine-tuning.
D) Implement a loss function that penalizes incorrect answers more heavily.
E) Apply data augmentation techniques to the training images, such as random cropping and rotations.

問題與答案：