AIF-C01-EXT-01_在线真题试卷与模拟练习_AIF-C01-EXT-01

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The marketing department at a retail company is working on a campaign for the upcoming Thanksgiving holidays. The department wants to exclude competitive brand names or sensitive topics from the content produced via generative AI prompts.

What type of prompting technique does the given use case represent?

A financial services company is exploring the use of generative AI to automate report generation and enhance customer insights. As part of this effort, the team is evaluating different AI models and technologies, including Foundation Models (FMs), to understand their capabilities and how they can be applied to improve efficiency. The team needs to clarify key characteristics and features of Foundation Models to determine if they align with the company's objectives.

Which of the following statements is correct regarding Foundation Models (FMs) in the context of generative AI?

A retail company is looking to optimize its supply chain planning and reduce stockouts. The team is exploring various AWS services to support this effort and is particularly interested in using machine learning for accurate resource planning. The team needs to ensure that the service is well-suited to address their specific use cases.

Which of the following is the best-fit for the Amazon Forecast service?

A retail company is looking to streamline its machine learning workflows and improve collaboration between data science teams. The team is considering using MLflow with Amazon SageMaker to manage the end-to-end machine learning lifecycle. Understanding how MLflow integrates with Amazon SageMaker will help the team decide if this combination is the right fit for their machine learning project management needs.

Given this context, which statement best defines the use of MLflow with Amazon SageMaker?

A technology consulting firm is working with clients to implement generative AI solutions and needs to help them understand the differences between various AI models. In particular, the firm is evaluating the use of Foundation Models (FMs) and Large Language Models (LLMs) for tasks such as text generation, image creation, and data summarization. The firm wants to clarify the distinctions between these two types of models, especially in terms of their design, capabilities, and application scope.

What is a key difference between Foundation Models (FMs) and Large Language Models (LLMs) in the context of generative AI?

Which of the following represents a valid use case for a generative AI-powered model?

A manufacturing company aims to leverage Amazon Bedrock to create a generative AI application that automates the monitoring of inventory levels, sales data, and supply chain information. The application should also recommend optimal reorder points and quantities to enhance operational efficiency.

What do you recommend?

The development team at an e-commerce company is considering using Amazon Personalize to create tailored experiences based on user behavior, such as purchase history and browsing patterns. To make an informed decision, the team needs a clear understanding of how Amazon Personalize works and how it generates these recommendations in real time.

Given this context, which statement best describes the Amazon Personalize service?

A software development company is building generative AI solutions for its clients, including applications for content creation and automated responses. As part of the development process, the team needs to clearly understand the distinctions between model inference and model evaluation to ensure accurate performance tracking and deployment. Model inference is required for generating outputs from the model, while model evaluation is necessary for assessing how well the model performs based on specific metrics.

Which of the following options best summarizes the differences between model inference and model evaluation in the context of generative AI?

A technology company is developing a natural language processing (NLP) solution and exploring different model architectures for tasks such as language translation, summarization, and text generation. The team is particularly interested in understanding how Transformer models, which are widely used in state-of-the-art generative AI applications, process and generate text. To ensure they select the right architecture for their use case, the company needs a clear explanation of how Transformer models function.

Which of the following best summarizes the way Transformer models work?

A technology company is utilizing multiple machine learning models across different departments, such as marketing, customer support, and product development, to address various business needs. To enhance overall performance, the company wants these models to learn from each other by sharing the latest data insights and patterns discovered by each model. The goal is to optimize the models' accuracy and efficiency by effectively using the most up-to-date information available from all sources.

Given this objective, which approach would be the most suitable for achieving cross-model optimization?

A healthcare company is considering leveraging generative AI to enhance its data analysis and patient care services. The team is exploring the AWS cloud environment as a platform for deploying generative AI solutions and wants to understand the key benefits of using AWS for these purposes. They are particularly interested in how AWS can support the scalability, security, and flexibility needed to integrate AI models into their existing workflows and handle large amounts of sensitive data.

What is one of the primary advantages of using generative AI in the AWS cloud environment?

A customer support company is using a large language model developed with Amazon Bedrock to enhance its automated chatbot's ability to understand and respond accurately to customer queries. To improve the chatbot's performance, the company wants the model to correctly identify the intent behind various user interactions, such as whether a user is asking for a refund, seeking product information, or needing technical support. To achieve this, the company decides to use few-shots prompting to train the model effectively.

Given this goal, what type of data should be included in the few-shots examples to help the model accurately recognize and distinguish the correct user intent?

A company has deployed several machine learning models on Amazon Bedrock to provide real-time predictions and analytics for its clients. To maintain operational transparency and ensure compliance with regulatory requirements, the company needs to monitor the input data sent to these models and the output responses generated. This monitoring is crucial for tracking usage, auditing access patterns, and troubleshooting any issues that may arise during model execution. The company is looking for a solution that provides detailed visibility into all model invocations to maintain effective oversight.

Which of the following solutions would be the most suitable for achieving this goal?

The marketing department at a media company wants to leverage Amazon Bedrock for making creative scripts for an upcoming ad campaign.

What do you recommend?

A Large Language Model (LLM) chatbot is generating responses that appear plausible and factual but are actually incorrect. What is this phenomenon called?

A legal research firm is seeking to implement a cutting-edge（先进的） AI solution that can generate detailed responses to complex legal queries by retrieving relevant information from their extensive database of legal documents. The company wants to use Amazon Bedrock to deploy a fully managed solution that supports an end-to-end Retrieval Augmented Generation (RAG) workflow, ensuring that the AI can pull contextually accurate information and generate high-quality answers efficiently.

What solution or approach would you recommend for implementing fully managed support for a RAG workflow in Amazon Bedrock?

Which of the following explanations BEST describes the differences between Shapley values and Partial Dependence Plots (PDP) in the context of model explainability, and how you might use them for this purpose?

A retail company is looking to implement AI-driven solutions to enhance its in-store operations, such as automating inventory management and improving security through visual monitoring. The company wants to leverage pre-trained computer vision (CV) models for fast deployment but also needs the flexibility to customize these models to fit specific business needs. To make an informed decision, the team is evaluating AWS services that offer both pre-trained and customizable computer vision capabilities.

What do you suggest?

Which of the following embedding models would be most suitable for differentiating the contextual meanings of words when applied to different phrases?

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