Question 1

You are developing a generative AI system that needs to create text-based narratives based on a sequence of images. Which approach would best handle this multimodal task while ensuring accurate context understanding and efficient processing?

Options :

A : Use a GAN to generate synthetic images and a rule-based system to generate text.

B : Use a Vision Transformer (ViT) to encode the images and a GPT model fine-tuned on narrative generation to generate the text.

C : Use a CNN to encode the images and an RNN to generate the text.

D : Use a ResNet model to encode the images and a BERT model to generate the text.

Answer: B

Question 2

When customizing an LLM for a specific industry task where quick deployment and computational efficiency are priorities, which of the following methods would be most appropriate?

Options :

A : Using the original LLM without any modifications to handle industry-specific tasks.

B : Fine-tuning an existing large model on a small, targeted dataset specific to the industry.

C : Training a new model from scratch using industry-specific data.

D : Applying data augmentation techniques on the industry-specific dataset without fine-tuning the model.

Answer: B

Question 3

You are developing a multimodal AI model that combines video data with sensor readings to monitor and predict equipment failures in an industrial setting. The model outputs a probability score indicating the likelihood of failure. However, your stakeholders are struggling to interpret these scores. Which visualization approach would be most effective in conveying the likelihood of equipment failure and the contributing factors?

Options :

A : Develop a gauge chart to display the real-time probability of failure, combined with a tooltip feature to explain the contributing factors

B : Use a scatter plot to show the relationship between sensor readings and the probability of failure

C : Use a line chart to display the probability scores over time

D : Implement a heatmap that shows the correlation between different sensor readings and the probability of failure

Answer: A

Question 4

You are part of a team developing a multimodal AI system that recommends music videos based on users' preferences, which include both visual aesthetics and musical genres. The system’s success depends on how well the dataset is curated and labeled with both visual and audio features. What is the most important step when curating and labeling the dataset for this recommendation system?

Options :

A : Prioritizing high-definition video content

B : Using short clips to save on storage space

C : Selecting only popular music videos for the dataset

D : Ensuring that both visual and audio elements are accurately labeled

Answer: D

Question 5

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

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