A company is developing an ML application. The application must automatically group similar customers and products based on their characteristics. Which ML strategy should the company use to meet these requirements?
Answer(s): A
Unsupervised learning is used to automatically group or cluster similar customers and products based on their characteristics without the need for labeled data. This strategy fits scenarios where the goal is to discover patterns or groupings in the data.
A company wants to improve its chatbot's responses to match the company's desired tone. The company has100 examples of high-quality conversations between customer service agents and customers. The company wants to use this data to incorporate company tone into the chatbot's responses.Which solution meets these requirements?
Answer(s): D
Bedrock’s fine-tuning lets you adapt a foundation model on your own conversation examples—in this case, the100 high-quality transcripts to instill your company’s tone directly into the model’s response generation.
An education provider is building a question and answer application that uses a generative AI model to explain complex concepts. The education provider wants to automatically change the style of the model response depending on who is asking the question. The education provider will give the model the age range of the user who has asked the question.Which solution meets these requirements with the LEAST implementation effort?
Answer(s): B
Adding a role description to the prompt is the simplest and most effective way to adjust the model's response style based on the user's age range. It requires minimal implementation effort and effectively tailors the output.The other options involve more complex processes, such as fine-tuning or additional reasoning steps.
A company plans to use a generative AI model to provide real-time service quotes to users.Which criteria should the company use to select the correct model for this use case?
For real-time service quotes, the critical requirement is low response time. A model with optimized inference speed and low latency ensures fast and efficient user interactions, making it the correct selection criterion.
Which ML technique ensures data compliance and privacy when training AI models on AWS?
Answer(s): C
Federated learning lets you train a global model across multiple data holders (for example, different AWSaccounts or edge devices) without moving raw data to a central location. Each participant trains locally on its own private dataset and only shares model updates, preserving data privacy and ensuring compliance.
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