SAA-C03 Exam Dumps : AWS Certified Solutions Architect - Associate (SAA-C03)

The key requirements are (1) MySQL compatibility for existing applications and (2) the ability to scale automatically during demand spikes for a transactional workload. Amazon Aurora (MySQL-compatible) satisfies compatibility while providing managed database capabilities designed for high performance and scalability. Using AWS Database Migration Service (DMS) is operationally efficient for migration because it supports moving data from on-premises databases to AWS with options for continuous replication to minimize downtime, which is commonly required for transactional systems.

Turning on Aurora Auto Scaling (for Aurora Replicas) allows the database tier to automatically add or remove read replicas based on demand, which helps absorb increased read traffic without manual intervention. This meets the “scale automatically” requirement more directly than simply scaling storage. (Write scaling is addressed through Aurora’s architecture and capacity planning; for many workloads, scaling reads is the dominant elasticity need.)

Option A only mentions configuring storage scaling, which does not address compute scaling during load increases; RDS storage autoscaling helps prevent running out of space but does not automatically add compute capacity in response to demand. Option B is incorrect because Amazon Redshift is a data warehouse designed for analytics/OLAP, not a transactional MySQL OLTP replacement, and mysqldump to Redshift does not preserve application compatibility. Option D changes the data model by migrating to DynamoDB, which is not MySQL-compatible and would require application redesign—violating the compatibility requirement.

Therefore, C best satisfies compatibility and automatic scaling needs while using managed services that reduce operational overhead during and after migration.

For infrequent or ad hoc querying of log data, Amazon S3 + Amazon Athena provides the most cost-effective, serverless, and scalable analytics solution.

From AWS Documentation:

“Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.”

(Source: Amazon Athena User Guide)

Why A is correct:

Amazon S3 offers durable, scalable, and cost-efficient storage.

Athena allows SQL-based querying on structured or semi-structured data like logs.

No need to provision or manage infrastructure.

Ideal for occasional querying at low cost.

Why the others are not optimal:

Option B: OpenSearch adds cost and is best for frequent, low-latency log querying.

Option C: RDS is not optimized for large-scale write-heavy log ingestion and costs more.

Option D: CloudWatch Logs is suitable for real-time monitoring, not for long-term storage and analytics of large log volumes.

To serve a static website hosted in Amazon S3 over HTTPS, you must use Amazon CloudFront because S3 does not natively support HTTPS for static website endpoints.

Steps to meet HTTPS requirement:

B. Create a CloudFront distribution and configure the S3 bucket as the origin. This enables global edge caching and performance optimization.

D. Attach an SSL/TLS certificate (typically from AWS Certificate Manager) to the CloudFront distribution to handle HTTPS connections.

S3 buckets used as static website hosts only support HTTP directly. While S3 supports HTTPS for REST API access, it does not support HTTPS on static website endpoints.

This setup aligns with security best practices and supports the Secure and Operational Excellence pillars of the AWS Well-Architected Framework.

???? References:

Hosting a static website using Amazon S3 and CloudFront

CloudFront + HTTPS with ACM