AWS Certified Cloud Practitioner

Explanation

Correct Answer: C. Asynchronous inference

Why Asynchronous Inference is the Right Choice:

1. Large Input Data (up to 1 GB): Asynchronous inference is designed to handle large payloads (up to 1 GB) that exceed the typical limits of real-time inference (usually up to 6 MB).

2. Long Processing Times (up to 1 hour): Real-time inference typically has timeout limits of 60 seconds, while asynchronous inference can handle processing times of up to 15 minutes (with the ability to extend for longer-running jobs).

3. Near Real-time Latency Requirement: Asynchronous inference provides near real-time results by queuing requests and processing them asynchronously, then delivering results via Amazon S3 or Amazon SNS notifications.

Why Other Options Are Incorrect:

• A. Real-time inference: Limited to smaller payloads (typically up to 6 MB) and shorter processing times (usually 60-second timeout). Cannot handle 1 GB files or 1-hour processing times.

• B. Serverless inference: Similar limitations to real-time inference in terms of payload size and processing time constraints.

• D. Batch transform: Designed for offline processing of large datasets, not for near real-time requirements. It processes data in batches with no latency guarantees.

Key Characteristics of Asynchronous Inference:

• Payload Size: Up to 1 GB
• Processing Time: Up to 15 minutes (configurable for longer)
• Result Delivery: Via Amazon S3 or Amazon SNS
• Use Cases: Large document processing, video analysis, genome sequencing, and other compute-intensive ML tasks

This solution allows the company to process large ML workloads while maintaining near real-time responsiveness through asynchronous processing patterns.