Fundamentals of Generative AI

A financial services company wants to build a chatbot that answers employee questions using their internal policy documents stored in Amazon S3. The team wants to minimize infrastructure management and avoid building a custom pipeline for chunking documents, generating embeddings, and storing them in a vector database. Which Amazon Bedrock capability best fits this need?

A company wants to give employees a conversational assistant that can answer questions using the company's internal documents stored across SharePoint, Confluence, and S3, while respecting existing user access permissions. The team wants a fully managed solution and prefers not to build or fine-tune their own model. Which AWS service best fits this requirement?

A contact center wants to help live support agents respond faster during customer calls. As the customer speaks, the company wants an AI tool that listens to the conversation, understands the caller's intent in real time, and automatically surfaces relevant knowledge-base articles and suggested responses to the agent. Which AWS generative-AI capability is purpose-built for this real-time agent-assist use case?

A software engineering team wants to accelerate development by using a generative-AI assistant that can generate code suggestions, explain existing code, and help identify security issues directly within their IDE. Which AWS service is purpose-built for this use case?

A software engineering team wants an AI assistant that is purpose-built to help developers write, debug, and explain application code directly inside their IDE, with minimal setup and no need to build a custom application. Which AWS generative-AI offering is designed specifically for this purpose?

A retail analytics team wants business users to ask questions about sales data in plain English (for example, 'What were the top 5 products by revenue last quarter?') and receive charts and narrative summaries directly inside their existing business intelligence dashboards. Which AWS generative-AI capability is designed for this use case?

A travel company wants to build a generative-AI assistant that can answer customer questions and also complete multi-step actions such as looking up a booking in an internal database, checking flight availability via an existing API, and confirming a rebooking. The team wants a foundation model to reason about which steps to take and automatically call the right internal APIs to fulfill the request. Which Amazon Bedrock capability is designed for this use case?

A startup wants to add generative-AI features to its customer support app by calling multiple third-party foundation models (from providers like Anthropic and Meta) through a single API. The team has no ML infrastructure and wants to avoid managing servers, provisioning GPUs, or hosting the models themselves. Which AWS service best meets these requirements?

A retail company is building a customer-facing chatbot on Amazon Bedrock. The chatbot must answer simple product questions with very low latency and at the lowest possible cost per request. The queries are short and do not require complex reasoning or long-form generation. The team is deciding which class of foundation model to select within Bedrock. Which selection approach best fits these requirements?

A financial services company wants to adapt a foundation model in Amazon Bedrock so it responds using the company's specific terminology and preferred answer style. They have a curated dataset of about 5,000 high-quality prompt-and-response pairs written by their domain experts. They want the model to learn to produce responses that match these labeled examples. Which Amazon Bedrock customization approach best fits this requirement?

A marketing team at a retail company spends many hours each week manually writing product descriptions, social media captions, and promotional email drafts for thousands of catalog items. Leadership wants to understand the primary business value that adopting a generative AI solution would bring to this workflow. Which statement best describes that business value?

A legal firm wants to use a foundation model on Amazon Bedrock to summarize entire contract documents. Some contracts are extremely long, and the team notices that when they paste a very large contract into a single prompt, the model returns an error or ignores the later portions of the document. What is the most likely cause of this behavior?

A media company wants to build a semantic search feature so that users can find articles related in meaning to a query, even when the exact keywords do not match. An AI engineer plans to convert each article and query into numerical vectors that capture semantic meaning, then compare them for similarity. Which generative AI concept is the engineer relying on?

A retail company wants to build an application where customers can upload a photo of a product and ask questions about it in natural language, receiving text answers that reference what is visible in the image. Which capability of a foundation model must the team prioritize when selecting a model on Amazon Bedrock?

A product manager at a media company is comparing two foundation models offered in Amazon Bedrock. One model has significantly more parameters than the other. The manager asks a colleague what having more parameters generally means for the model. Which statement best describes the role of parameters in a foundation model?

A startup wants to build a chatbot, a document summarizer, and a code-completion tool. Their small team has no ML expertise and cannot afford to train models from scratch. During planning, an engineer explains that a single type of model can serve as the starting point for all three applications because it was trained on vast, diverse data and can be adapted to many tasks. Which characteristic best describes this type of model?

A media company wants to build an application that summarizes long news articles written in multiple languages. The team is evaluating which type of model to use as the core of their solution on Amazon Bedrock. They want to minimize custom development and avoid training a model from scratch. Which approach best fits this requirement?

A telecommunications company handles thousands of customer support calls daily. Each call produces a lengthy transcript that agents must manually review to write a brief case summary before closing the ticket. Leadership wants to reduce the time agents spend on this task and improve consistency across summaries. Which characteristic of generative AI makes it the most suitable technology to deliver business value in this scenario?

A software company wants to speed up onboarding for new engineers who spend hours understanding a large legacy codebase written years ago with sparse documentation. Leadership asks how generative AI could deliver business value in this situation. Which use case best demonstrates the value of generative AI here?

A pharmaceutical research team wants to accelerate early-stage drug discovery by having a system propose novel candidate molecular structures that satisfy certain chemical property constraints, rather than manually enumerating known compounds. Which characteristic of generative AI makes it well suited to this task?