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A company is using a large language model (LLM) on Amazon Bedrock to build a chatbot. The chatbot processes customer support requests. To resolve a request, the customer and the chatbot must interact a few times.
Which solution gives the LLM the ability to use content from previous customer messages?
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B
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Explanation
The company is building a chatbot using an LLM on Amazon Bedrock, and the chatbot needs to use content from previous customer messages to resolve requests. Adding previous messages to the model prompt (also known as providing conversation history) enables the LLM to maintain context across interactions, allowing it to respond coherently based on the ongoing conversation. Exact Extract from AWS AI Documents: From the AWS Bedrock User Guide: "To enable a large language model (LLM) to maintain context in a conversation, you can include previous messages in the model prompt. This approach, often referred to as providing conversation history, allows the LLM to generate responses that are contextually relevant toprior interactions." (Source: AWS Bedrock User Guide, Building Conversational Applications) Detailed Explanation: Option A: Turn on model invocation logging to collect messages.Model invocation logging records interactions for auditing or debugging but does not provide the LLM with access to previous messages during inference to maintain conversation context. Option B: Add messages to the model prompt.This is the correct answer. Including previous messages in the prompt gives the LLM the conversation history it needs to respond appropriately, a common practice for chatbots on Amazon Bedrock. Option C: Use Amazon Personalize to save conversation history.Amazon Personalize is for building recommendation systems, not for managing conversation history in a chatbot. This option is irrelevant. Option D: Use Provisioned Throughput for the LLM.Provisioned Throughput in Amazon Bedrock ensures consistent performance for model inference but does not address the need to use previous messages in the conversation. [References:, AWS Bedrock User Guide: Building Conversational Applications (https://docs.aws.amazon.com/bedrock/latest/userguide/conversational-apps.html), AWS AI Practitioner Learning Path: Module on Generative AI and Chatbots, Amazon Bedrock Developer Guide: Managing Conversation Context (https://aws.amazon.com/bedrock/), , , , , ]
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A large retail bank wants to develop an ML system to help the risk management team decide on loan allocations for different demographics.
What must the bank do to develop an unbiased ML model?
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D
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Explanation
Class imbalance in a training dataset can cause ML models to favor overrepresented groups, leading to biased predictions. The AWS AI Practitioner guide and SageMaker Clarify documentation emphasize the need to identify and mitigate class imbalance to ensure fairness and unbiased model outcomes. D is correct: By measuring class imbalance and adapting the training process (e.g., through oversampling, undersampling, or using class weights), organizations can improve fairness and reduce bias across demographic groups. A (reducing data size) could worsen bias by removing potentially useful diverse data. B (consistency with historical results) might reinforce existing biases. C (separate models) is not scalable and can introduce other fairness issues. “To reduce bias, examine class imbalance in your training data and use techniques to ensure all groups are fairly represented.” (Reference: AWS SageMaker Clarify: Mitigating Bias, AWS Responsible AI) |
A company is using AI to build a toy recommendation website that suggests toys based on a customer's interests and age. The company notices that the AI tends to suggest stereotypically gendered toys.
Which AWS service or feature should the company use to investigate the bias?
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D
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Explanation
Comprehensive and Detailed Explanation From Exact AWS AI documents: Amazon SageMaker Clarify is designed to detect and explain bias in ML models and datasets. AWS Responsible AI guidance recommends Clarify to: Identify bias in predictions Analyze feature attribution Support fairness and ethical AI practices Why the other options are incorrect: Rekognition (A) analyzes images, not recommendation bias. Amazon Q Developer (B) assists developers with code. Comprehend (C) performs NLP tasks, not bias analysis. AWS AI document references: Amazon SageMaker Clarify Documentation Detecting Bias in AI Systems Responsible AI on AWS |
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Find answers to the most common questions about the Amazon Web Services AIF-C01 exam, including what it is, how to prepare, and how it can boost your career.
The Amazon Web Services AIF-C01 certification is a globally-acknowledged credential that is awarded to candidates who pass this certification exam by obtaining the required passing score. This credential attests and validates the candidates' knowledge and hands-on skills in domains covered in the Amazon Web Services AIF-C01 certification syllabus. The Amazon Web Services AIF-C01 certified professionals with their verified proficiency and expertise are trusted and welcomed by hiring managers all over the world to perform leading roles in organizations. The success in Amazon Web Services AIF-C01 certification exam can be ensured only with a combination of clear knowledge on all exam domains and securing the required practical training. Like any other credential, Amazon Web Services AIF-C01 certification may require periodic renewal to stay current with new innovations in the concerned domains.