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NVIDIA Generative AI Multimodal Sample Questions (Q61-Q66):

NEW QUESTION # 61
You're training a multimodal model to generate images from text prompts. The model architecture consists of a text encoder (Transformer) and an image decoder (GAN). After training, you observe that the generated images are highly realistic but often don't accurately reflect the details specified in the text prompt. What strategy would be MOST effective in improving the alignment between the text prompts and the generated images?

A. Introduce a contrastive loss that encourages the image embedding to be close to the text embedding of its corresponding prompt and far from the embeddings of other prompts.
B. Use a simpler text encoder.
C. Increase the capacity of the GAN's generator network.
D. Use a larger dataset of images for training the GAN.
E. Reduce the learning rate of the text encoder.

Answer: A

Explanation:
A contrastive loss explicitly encourages the model to learn a shared embedding space where images and their corresponding text prompts are close together, while unrelated images and prompts are pushed apart. This directly addresses the alignment problem. Increasing GAN capacity or dataset size might improve image quality, but not necessarily text-image alignment. Reducing the text encoder learning rate might slow down training but doesn't guarantee better alignment. A simpler encoder will likely hurt performance.

NEW QUESTION # 62
You are using the Stable Diffusion model for image generation. You want to generate an image of a 'cat wearing a hat in a cyberpunk city', but you are not satisfied with the initial results. Which of the following techniques could you use to refine the generated image and get closer to your desired outcome?

A. Increase the number of inference steps.
B. Decrease the CFG (Classifier-Free Guidance) scale.
C. Use a negative prompt to exclude unwanted elements or styles.
D. Reduce the number of inference steps.
E. Change the random seed to explore different variations.

Answer: A,C,E

Explanation:
Increasing the number of inference steps allows the diffusion process to refine the image more thoroughly. Using a negative prompt helps to guide the generation process by specifying what not to include in the image. Changing the random seed allows you to explore different variations of the same prompt, which can lead to more desirable results. Decreasing the CFG scale can reduce adherence to the prompt, and reducing the number of inference steps results in less refined images.

NEW QUESTION # 63
You're working on a project involving multimodal transfer learning for generating recipes from images of dishes and ingredient lists. You have a large dataset of images but a limited dataset of paired images and ingredient lists. You decide to leverage a pre-trained image model and a pre-trained text model. However, you are facing catastrophic forgetting after fine-tuning the models on the paired image and ingredient list dat a. Which of the following techniques would be MOST effective in mitigating catastrophic forgetting while adapting the pre-trained models to the new task?

A. Freeze the weights of the pre-trained models and only train a small adapter module that bridges the gap between the pre-trained features and the recipe generation task.
B. Train the entire model from scratch on the limited paired dataset.
C. Apply L1 regularization to the model weights.
D. Use a very high learning rate during fine-tuning.
E. Increase the batch size during fine-tuning.

Answer: A

Explanation:
Using adapter modules is a common technique to mitigate catastrophic forgetting. By freezing most of the pre-trained weights and only training a small adapter, you preserve the knowledge learned during pre-training while adapting the model to the new task. Training from scratch would negate the benefits of transfer learning. A high learning rate can exacerbate forgetting. L1 regularization can prevent overfitting but doesn't directly address forgetting. Increasing batch size might improve generalization but doesn't solve the core issue of catastrophic forgetting.

NEW QUESTION # 64
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. Perplexity
B. Inception Score
C. ROUGE (Recall-Oriented Understudy for Gisting Evaluation)
D. BLEU (Bilingual Evaluation Understudy)
E. Mean Squared Error (MSE)

Answer: C

Explanation:
ROUGE is a recall-based metric that effectively measures the overlap between the generated summary and reference summaries. It's well-suited for evaluating the content relevance of summaries. BLEU, while used for text generation, focuses on precision and might penalize summaries with different wording but similar meaning. Perplexity measures fluency but not relevance. MSE is inappropriate for text. Inception score is used primarily for images.

NEW QUESTION # 65
You are developing a multimodal model that combines text and tabular data for predicting customer churn. The text data consists of customer reviews, and the tabular data includes demographics and transaction history. You've preprocessed both datasets. Which of the following approaches would be the MOST effective for integrating these modalities?

A. Concatenate the raw text and tabular data into a single feature vector.
B. All of the above.
C. Train separate models for text and tabular data, then average their predictions.
D. Use a Transformer-based model to encode the text and a separate neural network for the tabular data, then fuse the embeddings.
E. Convert the text data into numerical features using techniques like TF-IDF, then concatenate these features with the tabular data.

Answer: D,E

Explanation:
Options C and D provides the most effective integration. Using a Transformer-based model for text allows it to capture complex relationships and dependencies in the text. A separate neural network handles tabular data effectively. Fusing the embeddings provides a unified representation. Option D is also valid because it allowst he model to incorporate the text and tabular data together as a single feature vector. Raw concatenation (A) is unlikely to work well. Averaging predictions (B) might not capture interactions between modalities.

NEW QUESTION # 66
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NCA-GENM Reliable Test Online & NCA-GENM Test Collection Pdf

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NVIDIA Generative AI Multimodal Sample Questions (Q61-Q66):

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