Question 1

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 2

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 3

Your company is developing a multimodal AI application that combines text, image, and audio inputs. As part of the rapid development process, you are tasked with integrating a text-to-image model from HuggingFace and experimenting with it to see if it meets your needs. How can you most efficiently pull in and start experimenting with a text-to-image model from HuggingFace using the Transformers API?

Options :

A : Utilize the transformers.Pipeline with the text-to-image task and load the model directly from the HuggingFace repository.

B : Download the model-s code and modify it to suit the text-to-image task within your application.

C : Use the AutoModelForVision class to load the text-to-image model and begin testing.

D : Use the AutoModelForImageAndText class to load the model and start experimenting.

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

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

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