AWS Certified Solutions Architect - Associate
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A solutions architect is designing a new service behind Amazon API Gateway. The request patterns for the service will be unpredictable and can change suddenly from 0 requests to over 500 per second. The total size of the data that needs to be persisted in a backend database is currently less than 1 GB with unpredictable future growth. Data can be queried using simple key-value requests.
Which combination of AWS services would meet these requirements? (Choose two.)
Other
Community
UAnonymous
A
AWS Fargate
B
AWS Lambda
C
Amazon DynamoDB
D
Amazon EC2 Auto Scaling
E
MySQL-compatible Amazon Aurora
Explanation:
Explanation
AWS Lambda (Option B) is the correct choice for the compute layer because:
- It provides automatic scaling from 0 to over 500 requests per second without any provisioning
- No need to manage servers or scaling policies
- Pay-per-use pricing model aligns with unpredictable traffic patterns
- Seamless integration with API Gateway
Amazon DynamoDB (Option C) is the correct choice for the database layer because:
- It's a fully managed NoSQL database that supports simple key-value queries
- Provides automatic scaling to handle unpredictable traffic patterns
- Can scale from zero to thousands of requests per second automatically
- Suitable for data less than 1 GB with unpredictable future growth
- Serverless architecture with pay-per-request pricing
Why not the other options:
- AWS Fargate (Option A): While serverless, it still requires container management and may not scale as quickly as Lambda from 0 to 500+ requests per second
- Amazon EC2 Auto Scaling (Option D): Requires provisioning and management of scaling policies, not ideal for sudden spikes from 0 to 500+ requests
- MySQL-compatible Amazon Aurora (Option E): While it can scale, it's a relational database and not optimized for simple key-value queries; DynamoDB is more suitable for this use case
The combination of AWS Lambda and Amazon DynamoDB provides a fully serverless, auto-scaling architecture that can handle unpredictable traffic patterns while efficiently managing simple key-value data queries.
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