A medical company is customizing a foundation model (FM) for diagnostic purposes. The company needs the model to be transparent and explainable to meet regulatory requirements.Which solution will meet these requirements?
Answer(s): B
Amazon SageMaker Clarify helps with transparency and explainability by generating metrics, reports, and examples that show how the model makes decisions, which is essential for meeting regulatory requirements.The other options are not directly related to improving the model's transparency or explainability.
A company wants to use a large language model (LLM) to generate product descriptions. The company wants to give the model example descriptions that follow a format.Which prompt engineering technique will generate descriptions that match the format?
Answer(s): D
Few-shot prompting provides the model with several example product descriptions that follow your desired format, enabling it to learn the pattern and generate new descriptions in the same style without further training.
A company has developed an ML model to predict real estate sale prices. The company wants to deploy the model to make predictions without managing servers or infrastructure.Which solution meets these requirements?
Amazon SageMaker provides a fully managed service for deploying machine learning models at scale without needing to manage servers or infrastructure. When you deploy a model using Amazon SageMaker endpoints, it handles all the infrastructure, scaling, and maintenance aspects. This allows the company to focus solely on making predictions and integrating the model into their application, without worrying about managing servers or clusters.Amazon SageMaker simplifies the deployment of ML models by providing scalable, secure, and serverless endpoints, which are ideal for serving predictions in real-time.
HOTSPOT (Drag and Drop is not supported)Select the correct AI term from the following list for each statement. Each AI term should be selected one time.Hot Area:
Answer(s): A
Simulates human problem-solving capabilities -> AIApplies data-driven learning techniques to make predictions -> MLFocuses on processing data through intricate neural networks -> Deep learningAI (Artificial Intelligence) is the broadest concept and includes simulating human-like reasoning and problem-solving.ML (Machine Learning) is a subset of AI focused on learning from data to make predictions or decisions.Deep learning is a subset of ML that uses neural networks with many layers to process complex data.
A bank is fine-tuning a large language model (LLM) on Amazon Bedrock to assist customers with questions about their loans. The bank wants to ensure that the model does not reveal any private customer data.Which solution meets these requirements?
Preprocessing your fine-tuning dataset to strip out names, account numbers, and other PII prevents the model from ever ingesting - or later regurgitating - private customer information. This data sanitization step is the most reliable way to guarantee that sensitive details aren’t embedded into the model.
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