A company is developing a product recommendation application by using a generative AI model. The company must minimize the application’s environmental impact.Which solution will meet these requirements?
Answer(s): A
Optimizing the model architecture for computational efficiency reduces the compute resources and energy required during inference, which directly lowers the environmental impact of running the generative AIapplication.
A company wants to use large language models (LLMs) with Amazon Bedrock to develop a chat interface for the company's product manuals. The manuals are stored as PDF files.Which solution meets these requirements MOST cost-effectively?
Answer(s): D
Using an Amazon Bedrock knowledge base allows the model to efficiently access relevant information from thePDF manuals when needed, reducing the cost compared to continuously fine-tuning a model or providing allPDFs as context in each prompt. This approach ensures that only necessary context is provided, making it cost-effective.
A company runs an application on servers in an Amazon VPC. The company’s application uses AmazonBedrock APIs for AI features. The company does not want API calls to travel across the public internet.Which solution will meet this requirement?
AWS PrivateLink lets the application in the Amazon VPC connect privately to Amazon Bedrock through interface VPC endpoints, so the API traffic does not traverse the public internet.
Which technique can a company use to lower bias and toxicity in generative AI applications during the post-processing ML lifecycle?
Human-in-the-loop (HITL) is a technique where human oversight is involved in the decision-making process of AI systems. In the context of generative AI applications, HITL can be used during the post-processing phase to identify and mitigate biases or toxic outputs. Humans can review and intervene when the model generates inappropriate or biased content, providing corrections or adjustments that help reduce the likelihood of toxicity and bias. This feedback loop helps refine and improve the model’s outputs over time.
A company is developing an ML model to predict customer churn.Which evaluation metric will assess the model's performance on a binary classification task such as predicting churn?
For binary classification tasks like churn prediction, the F1 score balances precision and recall into a single metric, making it ideal for evaluating how well the model identifies churners without over- or under-predicting.
A company stores customer data in OpenSearch. The company wants an AI solution to retrieve specific customer information from the stored data. The AI solution must convert queries into data requests and generate CSV files from the results. Then, the AI solution must upload the CSV files to Amazon S3.Which solution will meet these requirements in the MOST operationally-efficient way?
An AI agent can orchestrate multiple actions end to end, including translating user queries into OpenSearch requests, generating CSV files from the results, and uploading the files to Amazon S3, making it the most operationally efficient approach.
A large retailer receives thousands of customer support inquiries about products every day. The customer support inquiries need to be processed quickly. The company wants to implement Agents for Amazon Bedrock.What are the key benefits of using Amazon Bedrock agents that could help this retailer?
Answer(s): B
Amazon Bedrock agents help automate repetitive tasks and orchestrate complex workflows, which is ideal for handling thousands of customer support inquiries efficiently. This helps reduce response times and improves productivity. The other options do not directly address automation and orchestration of tasks for customer support.
A company is building a generative AI application to help customers make travel reservations. The application will process customer requests and invoke the appropriate API calls to complete reservation transactions.Which Amazon Bedrock resource will meet these requirements?
Agents in Amazon Bedrock are designed to interpret user requests, orchestrate workflows, and automatically invoke the appropriate APIs and services to complete tasks such as reservation transactions in a generative AIapplication.
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