Question 1

You are testing the effectiveness of a multimodal AI model designed to predict stock market trends using financial news articles, social media sentiment, and historical stock prices. The model performs well during periods of market stability but shows significant accuracy drops during market volatility. What approach should you take to improve the model's performance during volatile periods?

Options :

A : Reduce the complexity of the model to avoid overfitting to stable market conditions.

B : Incorporate additional data sources such as real-time trading volumes and macroeconomic indicators to provide more context during volatile periods.

C : Focus training exclusively on periods of market stability to ensure consistent accuracy.

D : Apply data augmentation techniques to artificially increase the size of the dataset during stable periods.

Answer: B

Question 2

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 3

In a scenario where an AI system is designed to automate document processing by classifying, summarizing, and extracting key information from large volumes of legal documents, which combination of techniques leveraging pretrained modern LLMs would be most effective?

Options :

A : Utilize a pretrained LLM solely for text classification, with separate models for summarization and key information extraction.

B : Use a rule-based system for text classification, followed by a simple RNN for summarization, and a pretrained LLM for question-answering.

C : Apply a CNN for text classification, a transformer model for summarization, and a simple linear model for question-answering.

D : Use a pretrained LLM for token classification, followed by text summarization, and then apply question-answering for key information extraction.

Answer: D

Question 4

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 5

You are collaborating with a team to deploy a multimodal AI application that analyzes large datasets and generates predictive models and visualizations. After the initial deployment, users report that the application’s performance degrades significantly when processing data from multiple sources simultaneously. The system becomes slow, and the quality of the visualizations decreases. What is the most effective approach to resolve this performance issue?

Options :

A : Implement data caching mechanisms to reduce the load on the system

B : Decrease the resolution of the visualizations to reduce processing demands

C : Reduce the number of data sources to prevent system overload

D : Manually balance the data load across the system components

Answer: A

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