DVA-C02 Question 66
Single answerYou are developing a serverless application that processes customer orders and stores them in a database. The application requires high availability, scalability, and low-latency performance. Additionally, the application must handle frequent reads and writes, and you need to ensure that querying customer orders based on customer ID is efficient. Which AWS data store is the most suitable for this use case?
- A
Amazon RDS with a MySQL database
- B
Amazon DynamoDB
- C
Amazon Redshift
- D
Amazon S3 with object storage for orders
Show answer and explanation
Correct answer: B
Explanation
For a serverless application requiring high availability, scalability, and low-latency performance with frequent reads and writes, Amazon DynamoDB is the most suitable option. It is a fully managed NoSQL database built for high-throughput workloads and key-value access patterns, making it ideal for querying customer orders by customer ID. Other options like Amazon RDS or Amazon Redshift are better suited for relational data or analytical queries, while Amazon S3 is designed for object storage rather than database-like access.
- A. Incorrect.
Amazon RDS with a MySQL database is a relational database service that is well-suited for structured data and complex queries. However, it may not scale as efficiently as DynamoDB for high-throughput workloads with frequent reads and writes.
- B. Correct.
Amazon DynamoDB is a fully managed NoSQL database designed for low-latency, high-throughput use cases. It is highly scalable and ideal for applications requiring efficient reads and writes using key-value access patterns, making it a perfect fit for this use case.
- C. Incorrect.
Amazon Redshift is a data warehouse service optimized for analytical queries and complex aggregations. It is not intended for real-time transactional workloads with frequent reads and writes.
- D. Incorrect.
Amazon S3 is a highly durable object storage service, but it is not optimized for use cases requiring frequent reads and writes or efficient querying of structured data like customer orders.