Exam AWS Certified AI Practitioner AIF-C01 topic 1 question 125 discussion

In Scenario 1, the company's chatbot requires immediate predictions to accurately interpret user intent during interactions. Implementing real-time inference ensures that the model processes each user input instantaneously, providing timely and relevant responses essential for effective communication. For Scenario 2, the data processing job involves handling large volumes of text data during specific periods, such as weekends. Utilizing batch transform allows the system to process this data in bulk, optimizing resource utilization and efficiency without the need for immediate results. In Scenario 3, the engineering team aims to develop an API capable of swiftly processing small text inputs and delivering prompt predictions. Adopting real-time inference enables the API to handle each request as it arrives, ensuring low-latency responses critical for user-facing applications.

real time batch real time chatbot (real-time, low-latency predictions): Real-time inference Data processing job (large datasets on weekends): Batch transform

Scenario 1: The company’s chatbot needs predictions from the LLM to understand users’ intent with minimal latency. Answer: Real-time inference Scenario 2: A data processing job needs to query the LLM to process gigabytes of text files on weekends. Answer: Batch transform Scenario 3: The company’s engineering team needs to create an API that can process small pieces of text content and provide low-latency predictions. Answer: Real-time inference