AWS Certified Solutions Architect - Associate
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A company has a small Python application that processes JSON documents and outputs the results to an on-premises SQL database. The application runs thousands of times each day. The company wants to move the application to the AWS Cloud. The company needs a highly available solution that maximizes scalability and minimizes operational overhead.
Which solution will meet these requirements?
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UAnonymous
A
Place the JSON documents in an Amazon S3 bucket. Run the Python code on multiple Amazon EC2 instances to process the documents. Store the results in an Amazon Aurora DB cluster.
B
Place the JSON documents in an Amazon S3 bucket. Create an AWS Lambda function that runs the Python code to process the documents as they arrive in the S3 bucket. Store the results in an Amazon Aurora DB cluster.
C
Place the JSON documents in an Amazon Elastic Block Store (Amazon EBS) volume. Use the EBS Multi-Attach feature to attach the volume to multiple Amazon EC2 instances. Run the Python code on the EC2 instances to process the documents. Store the results on an Amazon RDS DB instance.
D
Place the JSON documents in an Amazon Simple Queue Service (Amazon SQS) queue as messages. Deploy the Python code as a container on an Amazon Elastic Container Service (Amazon ECS) cluster that is configured with the Amazon EC2 launch type. Use the container to process the SQS messages. Store the results on an Amazon RDS DB instance.
Explanation:
Explanation
Correct Answer: B
Why Option B is correct:
- Maximizes scalability: AWS Lambda automatically scales based on the number of incoming JSON documents in the S3 bucket. As thousands of documents arrive daily, Lambda can process them concurrently without manual intervention.
- Minimizes operational overhead: Lambda is serverless, meaning AWS manages the infrastructure, scaling, and maintenance. No EC2 instances to manage, patch, or scale.
- Highly available: Lambda runs across multiple Availability Zones by default, and S3 provides 99.999999999% durability.
- Event-driven architecture: S3 triggers Lambda automatically when new JSON documents arrive, creating an efficient, event-driven workflow.
- Cost-effective: Pay only for compute time used, with no idle resource costs.
Why other options are incorrect:
Option A: Using multiple EC2 instances requires manual scaling, load balancing, and infrastructure management, increasing operational overhead. EC2 instances need patching, monitoring, and scaling configuration.
Option C: EBS Multi-Attach with EC2 instances is complex, not designed for document processing, and creates a single point of failure. EBS volumes are block storage, not optimal for JSON document storage compared to S3.
Option D: While ECS with EC2 launch type can scale, it requires managing EC2 instances, containers, and scaling policies. This has higher operational overhead than serverless Lambda.
Key AWS Services Used:
- Amazon S3: Durable, scalable object storage for JSON documents
- AWS Lambda: Serverless compute for processing documents
- Amazon Aurora: Highly available, scalable relational database
This solution provides the best combination of high availability, scalability, and minimal operational overhead.
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