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203. Frage
A company is introducing a new feature for its application. The feature will refine the style of output messages. The company will fine-tune a large language model (LLM) on Amazon Bedrock to implement the feature. Which type of data does the company need to meet these requirements?
Antwort: C
Begründung:
* Fine-tuning requires paired input-output examples to teach the model how to respond to inputs with desired styled outputs.
* Single inputs (A) or outputs (B) are insufficient.
* Separate, unpaired samples (D) don't establish the input-output mapping.
# Reference:
AWS Documentation - Preparing data for fine-tuning FMs
204. Frage
A company wants to display the total sales for its top-selling products across various retail locations in the past 12 months.
Which AWS solution should the company use to automate the generation of graphs?
Antwort: B
205. Frage
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?
Antwort: B
Begründung:
Adding a role description to the prompt context is a straightforward way to instruct the generative AI model to adjust its response style based on the user's age range. This method requires minimal implementation effort as it does not involve additional training or complex logic.
* Option B (Correct): "Add a role description to the prompt context that instructs the model of the age range that the response should target": This is the correct answer because it involves the least implementation effort while effectively guiding the model to tailor responses according to the age range.
* Option A: "Fine-tune the model by using additional training data" is incorrect because it requires significant effort in gathering data and retraining the model.
* Option C: "Use chain-of-thought reasoning" is incorrect as it involves complex reasoning that may not directly address the need to adjust response style based on age.
* Option D: "Summarize the response text depending on the age of the user" is incorrect because it involves additional processing steps after generating the initial response, increasing complexity.
AWS AI Practitioner References:
* Prompt Engineering Techniques on AWS: AWS recommends using prompt context effectively to guide generative models in providing tailored responses based on specific user attributes.
206. Frage
A company wants to build an ML application.
Select and order the correct steps from the following list to develop a well-architected ML workload. Each step should be selected one time. (Select and order FOUR.)
* Deploy model
* Develop model
* Monitor model
* Define business goal and frame ML problem
Antwort:
Begründung:
Explanation:
Building a well-architected ML workload follows a structured lifecycle as outlined in AWS best practices.
The process begins with defining the business goal and framing the ML problem to ensure the project aligns with organizational objectives. Next, the model is developed, which includes data preparation, training, and evaluation. Once the model is ready, it is deployed tomake predictions in a production environment. Finally, the model is monitored to ensure it performs as expected and to address any issues like drift or degradation over time. This order ensures a systematic approach to ML development.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
"The machine learning lifecycle typically follows these stages: 1) Define the business goal and frame the ML problem, 2) Develop the model (including data preparation, training, and evaluation), 3) Deploy the model to production, and 4) Monitor the model for performance and drift to ensure it continues to meet business needs." (Source: AWS AI Practitioner Learning Path, Module on Machine Learning Lifecycle) Detailed Explanation:
Step 1: Define business goal and frame ML problemThis is the first step in any ML project. It involves understanding the business objective (e.g., reducing churn) and framing the ML problem (e.g., classification or regression). Without this step, the project lacks direction. The hotspot lists this option as "Define business goal and frame ML problem," which matches this stage.
Step 2: Develop modelAfter defining the problem, the next step is to develop the model. This includes collecting and preparing data, selecting an algorithm, training the model, and evaluating its performance. The hotspot lists "Develop model" as an option, aligning with this stage.
Step 3: Deploy modelOnce the model is developed and meets performance requirements, it is deployed to a production environment to make predictions or automate decisions. The hotspot includes "Deploy model" as an option, which fits this stage.
Step 4: Monitor modelAfter deployment, the model must be monitored to ensure it performs well over time, addressing issues like data drift or performance degradation. The hotspot lists "Monitor model" as an option, completing the lifecycle.
Hotspot Selection Analysis:
The hotspot provides four steps, each with the same dropdown options: "Select...," "Deploy model," "Develop model," "Monitor model," and "Define business goal and frame ML problem." The correct selections are:
Step 1: Define business goal and frame ML problem
Step 2: Develop model
Step 3: Deploy model
Step 4: Monitor model
Each option is used exactly once, as required, and follows the logical order of the ML lifecycle.
References:
AWS AI Practitioner Learning Path: Module on Machine Learning Lifecycle Amazon SageMaker Developer Guide: Machine Learning Workflow (https://docs.aws.amazon.com/sagemaker
/latest/dg/how-it-works-mlconcepts.html)
AWS Well-Architected Framework: Machine Learning Lens (https://docs.aws.amazon.com/wellarchitected
/latest/machine-learning-lens/)
207. Frage
Which AW5 service makes foundation models (FMs) available to help users build and scale generative AI applications?
Antwort: C
Begründung:
Amazon Bedrock is a fully managed service that provides access to foundation models (FMs) from various providers, enabling users to build and scale generative AI applications. It simplifies the process of integrating FMs into applications for tasks like text generation, chatbots, and more.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Amazon Bedrock is a fully managed service that makes foundation models (FMs) from leading AI providers available through a single API, enabling developers to build and scale generative AI applications with ease." (Source: AWS Bedrock User Guide, Introduction to Amazon Bedrock) Detailed Option A: Amazon Q DeveloperAmazon Q Developer is an AI-powered assistant for coding and AWS service guidance, not a service for hosting or providing foundation models.
Option B: Amazon BedrockThis is the correct answer. Amazon Bedrock provides access to foundation models, making it the primary service for building and scaling generative AI applications.
Option C: Amazon KendraAmazon Kendra is an intelligent search service powered by machine learning, not a service for providing foundation models or building generative AI applications.
Option D: Amazon ComprehendAmazon Comprehend is an NLP service for text analysis tasks like sentiment analysis, not for providing foundation models or supporting generative AI.
Reference:
AWS Bedrock User Guide: Introduction to Amazon Bedrock (https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html) AWS AI Practitioner Learning Path: Module on Generative AI Services AWS Documentation: Generative AI on AWS (https://aws.amazon.com/generative-ai/)
208. Frage
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