最新的NVIDIA NCA-GENM免費考試真題

問題1

You are tasked with evaluating a multimodal A1 model that combines image and text inputs to generate product descriptions. You observe that the model performs well on common product categories (e.g., clothing, electronics) but struggles with niche categories (e.g., antique furniture, scientific instruments). Which of the following strategies would be MOST effective in improving the model's performance on niche categories?

A. Replace the image encoder with a more powerful architecture.

B. Fine-tune the model on a dataset specifically curated for niche product categories.

C. Implement data augmentation techniques to create synthetic data for niche categories.

D. Decrease the learning rate during training.

E. Increase the overall size of the training dataset.

正確答案: B

說明：（僅 Fast2test 成員可見）

問題2

You are building a text-to-image generation pipeline using CLIP and a diffusion model. After training, you notice that the generated images often lack the specific details mentioned in the text prompts. Which of the following strategies could you employ to improve the alignment between text and image?

A. All of the above.

B. Increase the number of diffusion steps during the image generation process.

C. Increase the number of layers in the I-I-Net architecture of the diffusion model.

D. Fine-tune the CLIP model on a dataset of text-image pairs relevant to your desired domain.

E. Use negative prompt engineering to guide the diffusion process away from undesired attributes.

正確答案: A

說明：（僅 Fast2test 成員可見）

問題3

A multimodal A1 model is trained on a dataset containing biased text and images. This bias leads to the model generating outputs that reinforce negative stereotypes. Which of the following steps are crucial for addressing and mitigating this bias during the model development lifecycle? (Select TWO)

A. Implementing model distillation to reduce the model size

B. Using adversarial training techniques to encourage fairness.

C. Collecting a more diverse and representative dataset.

D. Reducing the number of layers in the neural network.

E. Increasing the learning rate during training.

正確答案: B,C

說明：（僅 Fast2test 成員可見）

問題4

You are deploying a multimodal Generative A1 model on a cloud platform. The model takes video and text as input to generate video descriptions. The model's performance needs to be monitored to ensure it meets certain performance SLAs. Which of the following metrics are MOST crucial to monitor in a production environment to ensure both computational efficiency and output quality? (Select TWO)

A. GPU utilization.

B. Inference latency (time per request).

C. BLEU score (or similar text generation metric) for generated descriptions.

D. Number of lines of code in the model.

E. Model size on disk.

正確答案: B,C

說明：（僅 Fast2test 成員可見）

問題5

You are fine-tuning a large pre-trained language model for a specific downstream task. During training, you observe that the model performs well on the training data but generalizes poorly to the validation dat a. Which of the following strategies could help improve the model's generalization performance?

A. Increase the learning rate.

B. Increase the training data size by collecting more data.

C. Decrease the learning rate.

D. Increase the weight decay (L2 regularization).

E. Implement early stopping based on the validation loss.

正確答案: B,C,D,E

說明：（僅 Fast2test 成員可見）

問題6

Which of the following are key challenges specific to training multimodal models compared to unimodal models? (Select TWO)

A. The relative simplicity of unimodal model architectures.

B. The lack of readily available pre-trained models for different modalities.

C. Increased computational cost due to processing multiple data types.

D. Aligning and fusing information from different modalities with potentially different representations and noise characteristics.

E. The difficulty of evaluating the performance of multimodal models.

正確答案: C,D

說明：（僅 Fast2test 成員可見）

問題7

You are building a multimodal application that needs to understand both image and text dat a. You want to use a pre-trained model but fine-tune it for your specific task. Which of the following strategies is MOST effective for fine-tuning a large pre-trained multimodal model?

A. Fine-tune the entire model, including both text and image encoder layers, using a small learning rate.

B. Fine-tune only the image encoder layers, keeping the text encoder layers frozen.

C. Fine-tune the attention mechanism between the text and image encoders, while keeping the encoder weights frozen.

D. Fine-tune only the text encoder layers, keeping the image encoder layers frozen.

E. Train a new classification head from scratch on top of the frozen pre-trained model.

正確答案: A

說明：（僅 Fast2test 成員可見）

問題8

You're training a multimodal model for generating stories from images and audio. You use a Transformer architecture. During training, you notice that the model struggles to maintain long-range dependencies in the generated stories, leading to incoherent narratives. Which of the following techniques would be MOST effective in addressing this issue within the Transformer architecture?

A. Using a smaller embedding dimension.

B. Using only audio as input.

C. Removing the self-attention mechanism.

D. Incorporating positional encodings and increasing the attention window size.

E. Reducing the number of layers in the Transformer.

正確答案: D

說明：（僅 Fast2test 成員可見）

最新的NVIDIA Generative AI Multimodal - NCA-GENM免費考試真題

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