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NVIDIA Generative AI Multimodal Sample Questions (Q202-Q207):

NEW QUESTION # 202
You are building a text-to-image application using CLIP. You notice that the generated images often lack specific details mentioned in the text prompt. Which of the following techniques would be most effective in improving the fidelity and detail of the generated images, given the limitations of CLIP's text encoder?

A. Using a larger image decoder network with more parameters to add detail during the image generation process.
B. Increasing the temperature parameter of the diffusion model used in conjunction with CLIP to introduce more randomness and potentially more detail.
C. Applying prompt engineering techniques such as adding descriptive adjectives and context to the text prompt and fine-tuning the prompt with iterative feedback.
D. Reducing the number of training steps for the diffusion model to prevent overfitting to the training data and promote generalization.
E. Training a custom text encoder from scratch with a larger dataset specifically tailored to your application's domain.

Answer: C

Explanation:
Prompt engineering is the most practical and effective method for improving the fidelity of text-to-image generation with CLIP, without requiring extensive retraining or architecture changes. By carefully crafting and refining the text prompt, you can guide the generation process to produce images that more accurately reflect the desired details. Training a custom text encoder (A) is resource-intensive. While a larger image decoder (B) might help, it doesn't address the core issue of accurately capturing the prompt's meaning. Increasing temperature (D) can add randomness but not necessarily detail. Reducing training steps (E) could worsen performance.

NEW QUESTION # 203
Consider the following scenario: You're training a GAN for generating high-resolution images (e.g., 1024x1024). You notice that the training process is unstable, with the generator and discriminator constantly oscillating. Which of the following architectural modifications and training techniques could help stabilize the training process?

A. Increasing the learning rate of both the generator and discriminator.
B. Using ReLU activation functions in the discriminator.
C. Applying batch normalization in both the generator and discriminator.
D. Using Wasserstein GAN (WGAN) with gradient penalty (GP).
E. Replacing standard convolutional layers with transposed convolutional layers in the generator.

Answer: C,D

Explanation:
WGAN with gradient penalty (GP) addresses the instability caused by the Jensen-Shannon divergence used in standard GANs. Batch normalization can help stabilize training by reducing internal covariate shift. Transposed convolutions are a common practice but don't inherently stabilize training. Increasing the learning rate can exacerbate instability. ReLU activation can lead to vanishing gradients.

NEW QUESTION # 204
You're building a multimodal model that takes an image and a question as input and outputs an answer (Visual Question Answering - VQA). You find your model is heavily relying on the question type (e.g., 'What color is...' always predicts 'blue') and ignoring the image content. Select TWO of the following techniques that could help mitigate this 'language prior' problem.

A. Use a question-only baseline to explicitly measure the model's reliance on language priors and then penalize deviations from that baseline during training.
B. Increase the training data size by including more diverse images.
C. Balance the dataset by ensuring an equal number of correct answers for each question type.
D. Decrease the learning rate of the image encoder.
E. Replace the image encoder with a simpler architecture.

Answer: A,C

Explanation:
B and D are the best answers. Using a question-only baseline (B) allows you to directly quantify the model's reliance on language priors and then penalize the model for over-relying on them during training, encouraging it to pay more attention to the image. Balancing the dataset (D) by ensuring an equal number of correct answers for each question type makes it harder for the model to simply predict based on the question type alone. The image encoder shouldn't be replaced as that is needed in the task. More images wouldn't necessarily fix the data imbalance.

NEW QUESTION # 205
You're analyzing the performance of a generative A1 model that produces images from text prompts. You notice that the model struggles to generate images with specific objects mentioned in the prompt, even though these objects appear frequently in the training dataset.
Which of the following techniques could BEST address this issue?

A. Reducing the learning rate during training.
B. Increasing the batch size during training.
C. Adding more layers to the generative model's architecture.
D. Implementing a re-weighting scheme that gives higher weight to training examples containing the problematic objects.
E. Applying a larger kernel size in the convolutional layers of the model.

Answer: D

Explanation:
Re-weighting the training data to emphasize examples containing the underrepresented objects directly addresses the issue of the model not learning these objects effectively. Increasing batch size or adding layers might improve overall performance but doesn't specifically target the object representation problem. Reducing learning rate may lead to slower convergence. Increasing kernel size changes receptive field but does not address the imbalanced representation of objects.

NEW QUESTION # 206
You are tasked with analyzing a large dataset of images used for training a generative A1 model. The dataset contains noisy labels and varying image quality. Which of the following preprocessing steps are MOST crucial for improving the performance of your model?

A. Resizing all images to a fixed resolution (e.g., 256x256).
B. Converting all images to grayscale to reduce computational complexity.
C. Using a pre-trained image quality assessment model to filter out low-quality images.
D. Applying aggressive data augmentation techniques like random rotations and flips.
E. Implementing a label smoothing technique to mitigate the impact of noisy labels.

Answer: C,E

Explanation:
Label smoothing addresses noisy labels by preventing the model from becoming overconfident in incorrect labels. Filtering low- quality images improves the overall data quality and prevents the model from learning from corrupted data. While resizing and data augmentation can be beneficial, they are less critical than handling noisy labels and poor image quality in this scenario. Converting to grayscale might reduce computation, but could remove crucial color information for the generative model.

NEW QUESTION # 207
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