Question #1586
A solutions architect is designing a workload to track real-time user activity metrics for a cloud-based application. The application will ingest data via HTTP requests, aggregate metrics per user, and store the results. The architect must prioritize managed services to minimize operational overhead, with plans to add independent components for future features. Which solution meets these requirements MOST effectively?
Use Amazon API Gateway with AWS Lambda functions to ingest and process the data, then store the results in an Amazon DynamoDB table.
Use an Elastic Load Balancer with an Auto Scaling group of Amazon EC2 instances to handle incoming requests and process data. Store the aggregated metrics in an Amazon S3 bucket.
Use Amazon API Gateway with AWS Lambda functions to ingest and process the data, then store the results in a PostgreSQL database running on an Amazon EC2 instance.
Use an Elastic Load Balancer with an Auto Scaling group of Amazon EC2 instances to handle requests and process data. Store the metrics in an Amazon Elastic File System (EFS) shared storage.
Explanation
The correct answer is A because:
- Amazon API Gateway provides a managed HTTP endpoint for data ingestion, eliminating server management.
- AWS Lambda processes data serverlessly, auto-scaling with traffic and reducing operational tasks.
- Amazon DynamoDB offers low-latency, scalable storage for real-time metrics, ideal for high write throughput.
Other options are less optimal:
- B & D use EC2 instances (requires scaling/configuring servers) and S3/EFS (not optimized for real-time aggregation).
- C uses EC2 for PostgreSQL (adds database management overhead).
Key points:
1. Prioritize serverless/managed services (API Gateway, Lambda, DynamoDB) to reduce operations.
2. DynamoDB supports real-time writes and aggregations.
3. Decoupled architecture enables future component additions.
Answer
The correct answer is: A