A company wants to launch a new internal generative AI interface to answer user questions. The interface will be based on a popular open source large language model (LLM). Which combination of steps…

Question

Options

• AUse Amazon SageMaker JumpStart to deploy the LLM.
• BDownload the LLM as a .zip file. Deploy the LLM on a GPU-based Amazon EC2 instance.
• CCreate a frontend HTML interface that uses an Amazon API Gateway WebSocket API with AWS
• DUse Amazon QuickSight to create a UI to handle the user interaction.
• EUse Amazon Lex to create a UI to handle the user interaction.

• A
  71% (30)
• B
  14% (6)
• D
  10% (4)
• E
  5% (2)

Explanation

A (SageMaker JumpStart) eliminates the need to manually provision GPU infrastructure, configure ML environments, or manage model serving - it provides one-click deployment of popular open-source LLMs with managed scaling and endpoints. C (API Gateway WebSocket API with a frontend) pairs with that by providing a serverless, fully managed communication layer for real-time chat interactions, requiring no server management for the UI tier.

Why the distractors fail:

• B requires manually downloading, configuring, and maintaining GPU EC2 instances - maximum operational overhead with no managed ML serving layer.
• D Amazon QuickSight is a BI/analytics visualization tool, not designed for conversational AI interfaces.
• E Amazon Lex is built for structured chatbots with predefined intents and slots - it doesn't serve as a passthrough interface for a generative LLM and adds unnecessary configuration complexity.

Memory tip: Match the layer to the managed service - SageMaker JumpStart = "jump straight to a deployed model," and API Gateway WebSocket = serverless real-time channel. Both are fully managed, so you operate nothing. Any answer that involves raw EC2 or a BI tool is a red flag for high overhead.

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