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AWS Certified AI Practitioner · AWS
Select one answer.
Hotspot Solution:
| Workload | Recommended Inference Mode | Rationale |
|---|---|---|
| Team 1: Chatbot | Real-time inference | Chatbots require persistent low-latency interactive response times. |
| Team 2: Nightly Processing | Batch transform | Scheduled large-scale offline processing where immediate response is not required. |
| Team 3: Employee Assistant | Real-time inference | Interactive conversational AI queries require immediate feedback. |
Explanation: Real-time inference is suited for workloads requiring instant, interactive response times. Batch transform is optimal for offline, high-volume processing of static datasets.
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Below is the correct answer: Realtime Batch Realtime
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