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

Key Requirements:

High availability and automatic recovery.

Separate transactional and analytical queries with minimal performance impact.

Allow up to a 4-hour lag for analytical queries.

Analysis of Options:

Option A:

Multi-AZ deployments provide high availability but do not include read replicas for separating transactional and analytical queries.

Analytical queries on the secondary DB instance would impact the transactional workload.

Incorrect Approach:Does not meet the requirement of query separation.

Option B:

Multi-AZ DB clusters provide high availability and include a reader endpoint. However, these are better suited for Aurora and not RDS for MySQL.

Incorrect Approach:Not applicable to standard RDS for MySQL.

Option C:

Multiple read replicas allow separation of transactional and analytical workloads.

Queries can be pointed to a replica in a different AZ, ensuring no impact on transactional queries.

Correct Approach:Meets all requirements with high availability and query separation.

Option D:

Creating nightly snapshots and read-only databases adds significant operational overhead and does not support the 4-hour lag requirement.

Incorrect Approach:Not practical for dynamic query separation.

AWS Solution Architect References:

Amazon RDS Read Replicas

Multi-AZ Deployments

The correct recommendation is to useAWS Schema Conversion Tool AWS SCTtogether withAWS DMSand run afull load plus CDCmigration so Oracle and Aurora PostgreSQL stay synchronized during the staged application cutover. AWS prescriptive guidance for Oracle-to-Aurora PostgreSQL migration explicitly uses AWS SCT and AWS DMS for this pattern. Because the source workload has a high number of reads and writes, amemory-optimized replication instanceis the stronger fit for heavy replication activity. DataSync is not the service used for relational database migration. Selecting only the largest tables would not satisfy the requirement to keep all application data synchronized across the whole migration window.

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AWS Auto Scaling is the right answer because the requirement is to adjust capacity automatically based on a predictable time-based usage pattern. AWS documentation states that you can use scheduled scaling actions with Amazon EC2 Auto Scaling to change desired capacity at specific times or on recurring schedules. That makes it well suited for workloads that need more capacity during the day and less at night. Spot Instances and Reserved Instances are pricing models, not automation mechanisms for scheduled capacity changes. CloudFormation provisions infrastructure but does not itself optimize day-night scaling behavior. Therefore, AWS Auto Scaling is the correct service for this requirement.