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Design for New SolutionsmediumMultiple ChoiceObjective-mapped

SAP-C02 Design for New Solutions Practice Question

A company is migrating a monolithic e-commerce application to AWS. The application consists of a web frontend, a REST API, and a PostgreSQL database. The migration plan is to containerize the frontend and API using Amazon ECS with Fargate, and use Amazon RDS for PostgreSQL. The company expects variable traffic with peak loads during promotional events. The architecture must be highly available and cost-effective. The operations team wants to minimize manual scaling interventions. Which solution should a Solutions Architect recommend?

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

Deploy the API and frontend on Amazon ECS with Fargate. Use Application Auto Scaling with target tracking based on average CPU and memory utilization. Use Amazon RDS for PostgreSQL with Multi-AZ deployment and Auto Scaling for storage.

It combines ECS with Fargate for serverless containers, Application Auto Scaling with target tracking based on CPU and memory utilization for automatic scaling, and Amazon RDS for PostgreSQL with Multi-AZ deployment for high availability and storage auto scaling. This meets the requirements for variable traffic, high availability, cost-effectiveness, and minimal manual intervention. Option A is wrong because it suggests Amazon DynamoDB instead of PostgreSQL, which is unsuitable for the relational workload. Option C is wrong because it uses EC2 instances with Auto Scaling, which adds management overhead and is less cost-effective than Fargate. Option D is wrong because it relies on scheduled scaling, which cannot handle unexpected spikes in traffic.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Deploy the API and frontend on Amazon ECS with Fargate. Use Application Auto Scaling with target tracking based on average CPU and memory utilization. Use Amazon DynamoDB for the database with on-demand capacity mode.

    Why it's wrong here

    DynamoDB is not appropriate for a PostgreSQL workload; the application would require significant changes.

  • Deploy the API and frontend on Amazon ECS with Fargate. Use Application Auto Scaling with target tracking based on average CPU and memory utilization. Use Amazon RDS for PostgreSQL with Multi-AZ deployment and Auto Scaling for storage.

    Why this is correct

    ECS with Fargate and target tracking scaling provides automated, cost-effective scaling without over-provisioning.

  • Deploy the API and frontend on Amazon EC2 instances behind an Application Load Balancer. Use Auto Scaling groups with dynamic scaling policies. Use Amazon RDS for PostgreSQL with Multi-AZ deployment.

    Why it's wrong here

    EC2 instances require more operational overhead than Fargate; Fargate is more cost-effective for containerized workloads.

  • Deploy the API and frontend on Amazon ECS with Fargate. Configure a scheduled task to scale out during known promotional events. Use Amazon RDS for PostgreSQL with Multi-AZ deployment.

    Why it's wrong here

    Scheduled scaling does not handle unexpected traffic spikes; manual intervention is still needed.

Quick reference

AWS S3 Storage Class Comparison

Storage ClassMin DurationRetrievalUse Case
S3 StandardNoneImmediateFrequently accessed data
S3 Standard-IA30 daysImmediateInfrequent access, rapid retrieval
S3 One Zone-IA30 daysImmediateNon-critical infrequent data
S3 Intelligent-TieringNoneImmediate–hoursUnknown or changing access patterns
S3 Glacier Instant90 daysMillisecondsArchive with instant retrieval
S3 Glacier Flexible90 daysMinutes–hoursArchive, flexible retrieval
S3 Glacier Deep Archive180 daysHoursLong-term compliance archive

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