Question 1

You are working on a team developing a technical manual for a new generative AI software that uses multimodal inputs. Your role is to ensure that the manual accurately describes how to train the model with the correct input formats and parameters. Which aspect is most important to emphasize when documenting the training process for the generative AI model?

Options :

A : Software installation instructions

B : Model evaluation metrics

C : Hardware requirements

D : Input data preprocessing steps

Answer: D

Question 2

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 3

Which of the following best describes how a pretrained modern LLM can be leveraged to solve various NLP tasks such as token classification, text classification, summarization, and question-answering?

Options :

A : A pretrained LLM can be fine-tuned on specific datasets to perform token classification, text classification, summarization, and question-answering with high accuracy.

B : Pretrained LLMs require manual feature engineering to perform tasks such as token classification and summarization.

C : A pretrained LLM must be used in conjunction with RNNs to achieve effective text classification and summarization.

D : Pretrained LLMs are only useful for text generation and cannot be adapted to tasks like token classification or summarization.

Answer: A

Question 4

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

Question 5

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

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