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

1. You are building a multimodal model that combines text and image data to generate captions. The text encoder is a pre-trained BERT model, and the image encoder is a ResNet-50. You observe that the generated captions are heavily biased towards descriptions based on the text input, and the image information is not well represented. Which of the following techniques could you apply to improve the contribution of the image modality?

A) Increase batch size substantially.
B) Freeze the weights of the ResNet-50 image encoder
C) Decrease the dimensionality of the image embeddings using PCA
D) Increase the learning rate of the BERT text encoder
E) Apply a modality-specific loss weight, giving higher weight to the image loss during training-

2. Consider the following PyTorch code snippet used for training a Generative A1 model:

A) CUDAOOM error because gradients are accumulating without updating parameters.
B) The learning rate scheduler is not being used correctly.
C) The code is correct and will train the model efficiently.
D) The model parameters will not be updated correctly since optimizer.step() is called outside the loop.
E) The code will run, but it's computationally inefficient. Gradients should be zeroed before each backward pass.

3. When training a multimodal generative model for image captioning, you notice the model generates grammatically correct but generic and uninformative captions. Which technique is MOST likely to improve the in formativeness and specificity of the generated captions?

A) Decrease the learning rate during training.
B) Decrese the size of the vocabulary.
C) Employ a diverse beam search or sampling strategy during inference to encourage exploration of different caption possibilities.
D) Use beam search during inference with a large beam size.
E) Increase the size of the image encoder.

4. You're using Stable Diffusion with a custom prompt to generate images of landscapes. You notice that the generated images consistently lack detail and appear blurry, despite increasing the number of inference steps. Which of the following prompt engineering techniques, combined with appropriate parameter tuning, is MOST likely to address this issue and improve the image's sharpness and detail?

A) Using completely unrelated keywords to encourage the model to create something unique.
B) Decreasing the 'guidance_scale' to allow for more creative freedom.
C) Using a very short and general prompt to allow the model more freedom.
D) Adding keywords like 'photorealistic', 'high resolution', '8k', 'detailed', and adjusting the 'clip_skip' parameter.
E) Specifying 'oil painting' or another artistic style to mask the lack of detail.

5. In a multimodal sentiment analysis task involving text and images, you find that your model performs well on datasets with clear emotional cues in both modalities but struggles on datasets where the sentiment is subtle or requires nuanced understanding. Which of the following techniques would be MOST helpful in improving the model's performance on these more challenging datasets?

A) Increase the size of the training dataset.
B) Use a simpler model architecture.
C) Implement a contrastive learning approach, training the model to distinguish between samples with similar and dissimilar sentiments.
D) Reduce the learning rate.
E) Randomly flip the images during training

問題與答案：