A company is developing an ML model to make loan approvals. The company must implement a solution to detect bias in the model. The company must also be able to explain the model's predictions.Which solution will meet these requirements?
Answer(s): A
Amazon SageMaker Clarify is a tool that helps detect bias in machine learning models and provides explainability for model predictions. It allows users to understand the factors influencing a model's predictions and assess whether the model is fair across different demographic groups. This is exactly what the company needs for detecting bias and explaining model decisions, especially for high-stakes applications like loan approvals.-▪ Bias detection: SageMaker Clarify can analyze the training data and the model's predictions to identify and mitigate bias.-▪ Explainability: It provides features for explaining the predictions made by the model, which helps users understand the reasons behind the model's decisions.-
A manufacturing company uses AI to inspect products and find any damages or defects.Which type of AI application is the company using?
Answer(s): C
Computer vision is a field of AI that enables machines to interpret and understand visual information from the world, such as images and videos. In the case of the manufacturing company, the AI is used to inspect products for damages or defects, which involves analyzing visual data (e.g., product images or videos). This is a classic application of computer vision, where the AI system identifies and classifies objects or defects within images.
An AI practitioner is using a large language model (LLM) to create content for marketing campaigns. The generated content sounds plausible and factual but is incorrect.Which problem is the LLM having?
Answer(s): B
Hallucination occurs when a large language model generates content that appears plausible and factual but is incorrect or fabricated. This is a common issue with LLMs. The other options do not describe this particular behavior.
A company wants to use a large language model (LLM) on Amazon Bedrock for sentiment analysis. The company wants to classify the sentiment of text passages as positive or negative.Which prompt engineering strategy meets these requirements?
Providing examples with labels in the prompt helps the LLM understand the context of sentiment analysis,improving its accuracy in classifying the new text passage as positive or negative. The other options do not effectively guide the LLM for sentiment analysis.
A company built a Retrieval Augmented Generation (RAG) based AI assistant by using Amazon BedrockAgents. Sometimes the AI assistant gives incomplete or inaccurate answers. The company wants to improve and evaluate the AI assistant's performance.Which solution will meet these requirements?
Answer(s): D
A RAG-based assistant depends on both retrieval quality and generation quality. Confirming that the retrieved documents are relevant and that the final answers are accurate evaluates the full RAG workflow and helps identify whether incomplete or inaccurate responses are caused by poor retrieval, poor generation, or both.
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