Download Full Version AIF-C01 Amazon Web Services Exam

Amazon Bedrock is a service that provides foundational models for building generative AI applications. When creating an application for children, it is crucial to ensure that the generated content is appropriate for the target audience. "Guardrails" in Amazon Bedrock provide mechanisms to control the outputs and topics of generated content to align with desired safety standards and appropriateness levels.

Option C (Correct): "Guardrails for Amazon Bedrock":This is the correct answer because guardrails are specifically designed to help users enforce content moderation, filtering, and safety checks on the outputs generated by models in Amazon Bedrock. For a children’s application, guardrails ensure that all content generated is suitable and appropriate for the intended audience.

Option A:"Amazon Rekognition" is incorrect. Amazon Rekognition is an image and video analysis service that can detect inappropriate content in images or videos, but it does not handle text or story generation.

Option B:"Amazon Bedrock playgrounds" is incorrect because playgrounds are environments for experimenting and testing model outputs, but they do not inherently provide safeguards to ensure content appropriateness for specific audiences, such as children.

Option D:"Agents for Amazon Bedrock" is incorrect. Agents in Amazon Bedrock facilitate building AI applications with more interactive capabilities, but they do not provide specific guardrails for ensuring content appropriateness for children.

AWS AI Practitioner References:

Guardrails in Amazon Bedrock:Designed to help implement controls that ensure generated content is safe and suitable for specific use cases or audiences, such as children, by moderating and filtering inappropriate or undesired content.

Building Safe AI Applications:AWS provides guidance on implementing ethical AI practices, including using guardrails to protect against generating inappropriate or biased content.

The problem involves a simple probability calculation that can be handled efficiently by straightforward mathematical rules and computations. Using machine learning techniques would introduce unnecessary complexity and operational overhead.

Option C (Correct): "Use code that will calculate probability by using simple rules and computations":This is the correct answer because it directly solves the problem with minimal overhead, using basic probability rules.

Option A:"Use supervised learning to create a regression model" is incorrect as it overcomplicates the solution for a simple probability problem.

Option B:"Use reinforcement learning to train a model" is incorrect because reinforcement learning is not needed for a simple probability calculation.

Option D:"Use unsupervised learning to create a model" is incorrect as unsupervised learning is not applicable to this task.

AWS AI Practitioner References:

Choosing the Right Solution for AI Tasks:AWS recommends using the simplest and most efficient approach to solve a given problem, avoiding unnecessary machine learning techniques for straightforward tasks.

A summarization chatbot is ideal for extracting key points from legal documents. Large language models (LLMs) can be used to summarize complex texts, such as legal documents, making them more accessible and understandable.

Option C (Correct): "Develop a summarization chatbot":This is the correct answer because a summarization chatbot uses LLMs to condense and extract key information from text, which is precisely the requirement for reading and summarizing legal documents.

Option A:"Build an automatic named entity recognition system" is incorrect because it focuses on identifying specific entities, not summarizing documents.

Option B:"Create a recommendation engine" is incorrect as it is used to suggest products or content, not summarize text.

Option D:"Develop a multi-language translation system" is incorrect because translation is unrelated to summarizing text.

AWS AI Practitioner References:

Using LLMs for Text Summarization on AWS:AWS supports developing summarization tools using its AI services, including Amazon Bedrock.

Amazon QuickSight is a fully managed business intelligence (BI) service that allows users to create and publish interactive dashboards that include visualizations like graphs, charts, and tables. "Amazon Q" is the natural language query feature within Amazon QuickSight. It enables users to ask questions about their data in natural language and receive visual responses such as graphs.

Option C (Correct): "Amazon Q in Amazon QuickSight":This is the correct answer because Amazon QuickSight Q is specifically designed to allow users to explore their data through natural language queries, and it can automatically generate graphs to display sales data and other metrics. This makes it an ideal choice for the company to automate the generation of graphs showing total sales for its top-selling products across various retail locations.

Option A, B, and D:These options are incorrect:

A. Amazon Q in Amazon EC2:Amazon EC2 is a compute service that provides virtual servers, but it is not directly related to generating graphs or providing natural language querying features.

B. Amazon Q Developer:This is not an existing AWS service or feature.

D. Amazon Q in AWS Chatbot:AWS Chatbot is a service that integrates with Amazon Chime and Slack for monitoring and managing AWS resources, but it is not used for generating graphs based on sales data.

AWS AI Practitioner References:

Amazon QuickSight Qis designed to provide insights from data by using natural language queries, making it a powerful tool for generating automated graphs and visualizations directly from queried data.

Business Intelligence (BI) on AWS:AWS services such as Amazon QuickSight provide business intelligence capabilities, including automated reporting and visualization features, which are ideal for companies seeking to visualize data like sales trends over time.