SAA-C03 Leak Questions

AWS Control Tower provides a straightforward way to set up and govern a secure, multi-account AWS environment based on AWS best practices. It automates the setup of a baseline environment, or landing zone, that includes:

Service Control Policies (SCPs): These are used to manage permissions across AWS Organizations, allowing you to set permission guardrails. SCPs can restrict access to specific AWS services and actions, helping enforce security best practices.

Multi-Factor Authentication (MFA): AWS Control Tower can enforce MFA for the root user across all accounts, enhancing security.

Centralized Governance: It offers centralized logging and monitoring, making it easier to manage and audit multiple AWS accounts.

AWS Fargate with Spot capacity is the most cost-effective and serverless container option for short-duration jobs that are flexible on start time. Since the job is not latency-critical and runs nightly, it is a good candidate for Fargate Spot, which can offer up to 70% cost savings over On-Demand pricing.

The job runs for 1 hour, which exceeds the AWS Lambda maximum execution time (15 minutes). Therefore, Lambda is not suitable. EC2-based solutions involve higher operational overhead and cost, making Fargate Spot the best low-cost, low-maintenance solution.

AWS documentation states that the correct and least-privilege method for cross-account SNS-to-SQS integration is:

• Add specific SNS topic ARNs to the SQS queue policy.

• Allow only those topics to publish messages to the queue.

• Ensure SNS has permission to publish to the specific queue ARN.

This ensures strict scoping and adheres to least privilege.

Options A and D grant overly broad permissions. Option B allows publishing to any queue, which violates least privilege.

To address the high CPU utilization on the EC2 instance and the degraded performance of Amazon RDS for read requests, the solution involves two key actions: resizing the EC2 instance and leveraging Amazon RDS read replicas.

Resizing the EC2 Instance: The first step is to resize the EC2 instance to a type with more CPU capacity to handle the higher computational demands during peak usage times. This helps to alleviate the immediate pressure on the CPU.

Auto Scaling Group with a Size of 1: Although the application can only run on a single EC2 instance due to its monolithic nature, creating an Auto Scaling group with a minimum and maximum size of 1 ensures that the instance is automatically restarted or replaced in case of failure, maintaining high availability.

RDS Read Replica: Configuring an RDS read replica allows the application to offload read requests to a separate instance, thus reducing the load on the primary RDS instance. This improves the performance of read operations, which were previously bottlenecked due to the high CPU usage on the EC2 instance.

Why Not Other Options?:

Option B: Redirecting all traffic to the RDS read replica is not recommended because replicas are meant for read traffic only, not for write operations. This could lead to data consistency issues.

Option C: Increasing the RDS instance type capacity helps, but it doesn’t address the high CPU usage on the EC2 instance, nor does it provide a solution for scaling reads.

Option D: While resizing both the EC2 and RDS instances increases their capacities, it doesn’t address the specific need to offload read traffic from the primary RDS instance.

AWS References:

Amazon RDS Read Replicas- Explains how to create and use read replicas to offload read traffic from the primary database instance.

Resizing Your EC2 Instance- Guidance on resizing EC2 instances to meet workload demands.