Question 1

Which of the following best describes the role of the denoising diffusion process in enhancing the quality of images generated by diffusion models?

Options :

A : The denoising diffusion process iteratively refines the image by reducing noise and enhancing details over multiple steps, leading to higher quality outputs.

B : The denoising diffusion process applies a single pass of noise reduction to the final image, enhancing sharpness and clarity.

C : The denoising diffusion process is used only at the beginning of image generation to set an initial low-noise state.

D : The denoising diffusion process relies on pre-generated noise patterns to create detailed images.

Answer: A

Question 2

You are assisting with the fine-tuning of a multimodal AI model that processes images and corresponding descriptive text to perform sentiment analysis. After several epochs, the model starts to show signs of overfitting, with the training accuracy increasing while validation accuracy plateaus. The senior team member asks for your input on how to address this issue. What optimization technique should you recommend?

Options :

A : Decrease the learning rate to avoid overfitting.

B : Add more layers to the model to capture more complex patterns.

C : Increase the number of epochs to ensure the model learns from the training data.

D : Implement dropout in the fully connected layers of the model.

Answer: D

Question 3

A tech company is deploying a multimodal AI system that combines video surveillance, audio analysis, and motion sensors to enhance security in a large industrial facility. The system works well during the day but struggles at night when there is limited lighting and more background noise. Additionally, the system consumes a significant amount of energy, leading to higher operational costs. What is the most likely cause of the system's poor performance and high energy consumption at night?

Options :

A : The video model is not optimized for low-light conditions.

B : The multimodal fusion strategy is not adapting to different environmental conditions effectively.

C : The AI models are running continuously at full capacity without energy optimization.

D : The system’s audio model is overfitting to daytime noise patterns.

Answer: B

Question 4

While testing a multimodal AI model designed to generate captions for images, you observe that the generated captions are often too literal, failing to capture the nuances or context of the images. Which strategies should you consider to improve the quality of the generated captions? (Select two)

Options :

A : Incorporate a reinforcement learning approach with human feedback

B : Reduce the size of the training dataset

C : Train the model with a higher learning rate

D : Increase the diversity of the training dataset

E : Use an image feature extraction model trained on a similar domain

Answer: A,D

Question 5

You are designing a multimodal AI system that integrates visual data from medical images (e.g., X-rays) with corresponding patient notes to assist in diagnosing conditions. The system often overlooks critical details in the images, resulting in inaccurate diagnoses. How can you modify the system to improve its accuracy by ensuring it pays closer attention to relevant parts of the medical images?

Options :

A : Increase the resolution of input images before processing

B : Use a pre-trained image classification model without fine-tuning it on medical images

C : Implement a spatial attention mechanism that highlights critical regions in the medical images

D : Apply data augmentation techniques to increase the diversity of the training data

Answer: C