NCA-GENM 무료 덤프문제 온라인 액세스

You are building a multimodal generative A1 model that combines text, images, and audio. You notice that the model performs well on text and images but struggles with audio, particularly in noisy environments. Which of the following strategies would be MOST effective in improving the model's performance with audio data?

• A.Apply data augmentation techniques specifically designed for audio, such as adding noise or varying the speed and pitch.
• B.Decrease the weight of the audio modality in the loss function.
• C.Use transfer learning by pre-training the audio component of the model on a large audio dataset.
• D.Reduce the dimensionality of the audio features to simplify the learning task.
• E.Increase the learning rate for the audio modality during training.

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

You are building a generative AI model that creates realistic product designs based on textual descriptions and a reference image depicting a similar, but not identical, product. You are using a Variational Autoencoder (VAE) architecture. However, the generated images lack the fine-grained details present in the reference image. Which of the following methods would be most suitable to incorporate fine-grained details from the reference image into the generated design?

• A.Replace the VAE with a Generative Adversarial Network (GAN).
• B.Use a larger convolutional kernel Size in the decoder
• C.Reduce the batch size during training.
• D.Increase the latent space dimensionality of the VAE.
• E.Implement a skip connection from the encoder of the reference image to the decoder of the generative model, allowing the decoder to directly access features from the reference image at multiple scales.

You're working with a client to develop a generative A1 model for creating personalized marketing content. During requirements acquisition, the client expresses a desire for 'highly creative' and 'unique' outputs. However, they struggle to articulate specific aesthetic preferences. How would you best approach translating these subjective requirements into concrete model training and prompt engineering strategies?

• A.Focus solely on quantitative metrics like perplexity and FID score to ensure the model generates diverse and high-quality content, assuming that 'creative' and 'unique' will naturally emerge.
• B.Implement a system for interactive prompt refinement, allowing the client to iteratively modify prompts and observe the resulting outputs in real-time, facilitating a collaborative exploration of the model's creative potential.
• C.Conduct extensive A/B testing with a large user group, presenting them with various model outputs and gathering feedback on which content they perceive as most 'creative' and 'unique'. Use this feedback to refine the model and prompts.
• D.B and D
• E.Use a pre-trained style transfer model to apply different artistic styles to the generated content, offering the client a diverse range of options to choose from and identify their preferred aesthetic.

You are experimenting with different loss functions for training a Variational Autoencoder (VAE) to generate images. You observe that using only the reconstruction loss (e.g., Mean Squared Error) results in blurry images. What other loss component is typically added to the VAE objective function to encourage the latent space to be well-structured and generate sharper images?

• A.Cross-entropy loss
• B.Contrastive loss
• C.Kullback-Leibler (KL) divergence loss
• D.Perceptual loss
• E.Hinge loss