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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 multimodal generative A1 model that takes both image and text as input to generate a coherent video. Which of the following architectures is MOST suitable for this task, considering the need to fuse information from different modalities and generate sequential data?

A) A Generative Adversarial Network (GAN) trained solely on image data and later fine-tuned with text embeddings.
B) A Transformer-based architecture with separate encoders for image and text, followed by a decoder that generates video frames.
C) A standard Convolutional Neural Network (CNN) followed by a fully connected layer.
D) A Support Vector Machine (SVM) classifier trained to predict the next frame based on image and text features.
E) A simple recurrent neural network (RNN) that concatenates image feature vectors and text embeddings as input at each time step.

2. You are working on a generative A1 model that creates descriptions of images. During experimentation, you notice the model consistently generates descriptions that are factually incorrect about objects in the image, despite the image quality being high. For example, it might describe a 'cat' as a 'dog'. What is the MOST critical step to address this issue?

A) Apply image sharpening filters to the input images.
B) Fine-tune the model using a smaller learning rate.
C) Increase the training data size with more diverse images.
D) Use a more complex model architecture.
E) Implement a mechanism to verify the generated descriptions against an external knowledge base or object recognition system.

3. You are designing an experiment to compare two different multimodal A1 model architectures for video summarization. Model A is a transformer-based model, and Model B is a recurrent neural network (RNN)-based model. Which of the following evaluation metrics would be MOST appropriate for comparing the quality of the generated summaries, considering both content relevance and fluency?

A) ROUGE (Recall-Oriented Understudy for Gisting Evaluation)
B) BLEU (Bilingual Evaluation Understudy)
C) Mean Squared Error (MSE)
D) Perplexity
E) Inception Score

4. Consider the following Python code snippet, which attempts to implement a basic form of cross-validation. What is the primary issue with this code and how would you fix it to prevent data leakage?

A) Data leakage occurs because feature scaling is applied to the entire dataset before splitting it into training and testing sets. Fix: Apply scaling separately to the training and testing sets within each fold.
B) Data leakage occurs because the model is not being evaluated on a hold-out set Fix: Create a separate validation set and evaluate the model on it after each fold.
C) Data leakage occurs because the 'KFold" split is not shuffled. Fix: Set in the "KFold' constructor.
D) Data leakage occurs because the model is being reinitialized in each fold. Fix: Move the model initialization outside the loop.
E) The code is correct and doesn't have any data leakage issues.

5. Consider the following Python code snippet using PyTorch, intended for fusing image and text features in a multimodal model. Assume 'image_featureS and 'text_features' are tensors of shape Which of the following fusion methods is implemented in this code?

A) Tensor Product
B) Cross-modal Attention
C) Element-wise Addition
D) Concatenation
E) Gated Attention

問題與答案：