Amazon Web Services SAA-C03 Exam With Confidence Using Practice Dumps

This solution offers the least operational overhead while meeting the security and global availability requirements:

Amazon CloudFront and S3: Hosting the static frontend on S3 and serving it via CloudFront provides low-latency global distribution, high availability, and security. S3 is a cost-effective and serverless option for hosting static assets, and CloudFront ensures that the application is cached closer to the users, reducing latency globally.

AWS Lambda and API Gateway: Refactoring the microservices to use Lambda functions with API Gateway allows for a fully serverless, scalable, and highly available backend. This reduces the need for managing EC2 instances, as Lambda automatically scales to meet demand and only charges for the actual usage.

RDS for MySQL: Migrating the MySQL database from an EC2 instance to Amazon RDS significantly reduces operational overhead. RDS manages backups, patching, and scaling, and it offers high availability options (e.g., Multi-AZ).

Option AandDinvolve using EC2 Reserved Instances for the database, which requires more operational maintenance than using RDS.

Option Csuggests using a Network Load Balancer with Lambda, which adds unnecessary complexity for this use case.

AWS References:

Amazon S3 and CloudFront Integration

AWS Lambda with API Gateway

Amazon RDS for MySQL

This solution leverages:

Amazon Redshift Spectrum to query S3 data directly from Redshift.

Amazon Athena for ad-hoc analysis of S3 data.

Amazon QuickSight for unified visualization from multiple data sources.

“Redshift Spectrum enables you to run queries against exabytes of data in Amazon S3 without having to load or transform the data.”

“QuickSight supports both Amazon Redshift and Amazon Athena as data sources.”

— Redshift Spectrum

— Amazon QuickSight Supported Data Sources

This architecture allows scalable querying and visualization with minimum ETL overhead, ideal for BI dashboards.

Incorrect Options:

A: The query editor is not a BI tool.

B, D: Grafana is better for time-series data, not structured analytics or BI reports.

Option A is the best answer because it introduces both decoupling and horizontal scalability with minimal infrastructure management. API Gateway provides a managed front door for the microservices, Lambda provides serverless compute that scales with request volume, and SQS provides durable buffering between transactional stages such as order creation and payment processing. AWS recommends queue-based decoupling when producers and consumers operate at different speeds or when spikes must be absorbed without losing work. The EKS and EC2 alternatives add significantly more operational burden, and caching does not solve the core workflow-scaling issue. Therefore, a serverless microservices architecture using API Gateway, Lambda, and SQS is the best fit.

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