Amazon Web Services SAP-C02 - AWS Certified Solutions Architect - Professional

AWS Control Tower provides a set of " strongly recommended guardrails " that can be enabled to implement governance and policy enforcement. One of these guardrails is " Encrypt Amazon RDS instances " which will detect RDS DB instances that are not encrypted at rest. By enabling this guardrail and applying it to the production OU, the company will be able to enforce encryption for RDS instances in the production environment.

https://docs.aws.amazon.com/autoscaling/ec2/userguide/create-asg-instance-type-requirements.html#use-attribute-based-instance-type-selection-prerequisites

Enable DynamoDB Auto Scaling:

Navigate to the DynamoDB console and select the table experiencing high demand.

Go to the " Capacity " tab and enable auto scaling for both read and write capacity units. Auto scaling adjusts the provisioned throughput capacity automatically in response to actual traffic patterns, ensuring the table can handle the increased load.

Configure Auto Scaling Policies:

Set the minimum and maximum capacity units to define the range within which auto scaling can adjust the provisioned throughput.

Specify target utilization percentages for read and write operations, typically around 70%, to maintain a balance between performance and cost.

Monitor and Adjust:

Use Amazon CloudWatch to monitor the auto scaling activity and ensure it is effectively handling the increased demand.

Adjust the auto scaling settings if necessary to better match the traffic patterns and application requirements.

By enabling DynamoDB auto scaling, you ensure that the database can handle the fluctuating traffic volumes without manual intervention, improving response times and reducing errors.

References

AWS Compute Blog on Using API Gateway as a Proxy for DynamoDB【60】.

AWS Database Blog on DynamoDB Accelerator (DAX)【59】.

Option A is correct because IAM Access Analyzer can use the AWS Organizations organization as the zone of trust and generate findings for resources shared with principals outside that trusted boundary. This directly addresses unintended external sharing and continuous monitoring of resource-based policies. EventBridge can notify security teams when Access Analyzer creates new findings. The aws:MultiFactorAuthPresent condition key in role trust policies enforces MFA when users assume cross-account IAM roles. Option B is weaker because AWS Config is not purpose-built for external access analysis, and aws:TokenIssueTime does not enforce MFA. Option C uses ExternalId, which helps third-party delegation but does not solve broad monitoring or MFA enforcement. Option D adds federation but does not directly provide the required resource-policy analysis and trust-policy MFA control.

The correct answer is B.

B. This solution is the most cost-effective because it uses an EC2 Instance Savings Plan for the online gaming application instances, which provides the lowest prices and savings up to 72% compared to On-Demand prices. The EC2 Instance Savings Plan applies to any instance size within the same family and region, regardless of availability zone, operating system, or tenancy. The online gaming application instances run all year and must always be available, so they are not suitable for Spot Instances, which can be interrupted with a two-minute notice. This solution also uses Spot Instances for the analytics application, which can reduce the cost by up to 90% compared to On-Demand prices. The analytics application can be interrupted and resumed without issue, so it is a good fit for Spot Instances, which use spare EC2 capacity.This solution does not require AWSService Catalog, which is a service that helps to create and manage catalogs of IT services that are approved for use on AWS, but does not provide any discounts123

A. This solution is incorrect because it uses On-Demand Instances for the analytics application, which are more expensive than Spot Instances. The analytics application can be interrupted and resumed without issue, so it can benefit from the lower cost of Spot Instances, which use spare EC2 capacity.

C. This solution is incorrect because it uses Spot Instances for the online gaming application, which can be interrupted with a two-minute notice. The online gaming application instances must always be available, so they are not suitable for Spot Instances, which use spare EC2 capacity. This solution also uses AWS Service Catalog, which is a service that helps to create and manage catalogs of IT services that are approved for use on AWS, but does not provide any discounts.

D. This solution is incorrect because it uses On-Demand Instances for the online gaming application, which are more expensive than an EC2 Instance Savings Plan. The online gaming application instances run all year and must always be available, so they are suitable for an EC2 Instance Savings Plan, which provides the lowest prices and savings up to 72% compared to On-Demand prices. This solution also uses AWS Service Catalog, which is a service that helps to create and manage catalogs of IT services that are approved for use on AWS, but does not provide any discounts.

Comprehensive and Detailed Explanation:

Option C is the most comprehensive and aligns with AWS best practices for cost optimization without compromising availability or performance:

AWS Compute Optimizer provides recommendations to rightsize EC2 instances, EBS volumes, and Lambda functions based on utilization metrics. By opting in, the company can receive tailored suggestions for optimizing resource configurations.

AWS Trusted Advisor offers cost optimization checks, including identifying idle or underutilized resources, which can be rightsized or terminated to reduce costs.

Implementing recommendations from Compute Optimizer and Trusted Advisor ensures that resources are appropriately sized, leading to cost savings without affecting performance.

Auto Scaling groups help maintain application availability and allow automatic scaling of EC2 instances based on demand, ensuring that compute capacity aligns with workload requirements.

Compute Savings Plans provide flexible pricing for EC2, AWS Lambda, and AWS Fargate usage, offering significant savings over On-Demand pricing in exchange for a commitment to a consistent amount of usage.

This approach ensures cost optimization while maintaining the necessary compute capacity and availability for production workloads.

Option C is correct because AWS Systems Manager Patch Manager is the AWS-native service for scalable patch management across managed instances. Patch baselines define approved patches, while delegated administration and OU targeting allow centralized control across many accounts in AWS Organizations. Applying patches first to the non- production OU and then to the production OU with approval gates satisfies the security policy and reduces rollout risk. Systems Manager Compliance and AWS CloudTrail provide auditability for patch status and administrative actions. Option A relies heavily on custom Automation and EventBridge logic, which increases overhead. Option B requires account-by-account configuration and does not scale cleanly across thousands of instances. Option D uses AWS Config for detection, but Config does not provide full patch orchestration by itself.

The workload is a real-time leaderboard that needs extremely low latency: microsecond reads and single-digit millisecond writes. It also needs rapid failover (accepting writes in less than a minute after a primary node failure) and minimal operational overhead. Additionally, the data must be durable enough to persist and be used later for analytics through a data pipeline.

Amazon MemoryDB for Redis is a Redis-compatible, durable, in-memory database service designed for microsecond read latency and low-millisecond write latency while also providing durability through a distributed transaction log. MemoryDB is designed to be a primary database rather than a cache and supports Multi-AZ configurations with fast failover. These characteristics match the leaderboard’s latency requirements and the availability requirement to resume writes quickly after a failure. Because MemoryDB is fully managed, it has less operational overhead than running Redis on EC2.

Option C is the best choice because MemoryDB provides Redis compatibility, in-memory performance, Multi-AZ high availability, and durability that supports data persistence for downstream processing. The durability aspect is especially important for feeding analytics pipelines, because the data is not only cached but is retained as a database record.

Option A is not the best choice because Amazon ElastiCache for Redis is primarily used as a cache. While it can be configured for replication and can achieve low latency, it is not positioned as a durable primary data store in the same way as MemoryDB for Redis. For requirements that explicitly combine extremely low latency with persistence and database-like durability, MemoryDB is the more appropriate managed service with lower operational overhead than building custom durability mechanisms.

Option B is not suitable because Amazon RDS for MySQL typically cannot meet microsecond read latency, and write latencies are usually higher than single-digit milliseconds for non-trivial workloads. Also, read replicas do not provide multi-writer capability and do not ensure sub-minute write availability in all failure scenarios without additional architecture and failover handling.

Option D has the highest operational overhead because it requires the company to operate Redis on EC2: instance management, patching, scaling, replication, failover automation, monitoring, and backup/restore processes. This does not meet the requirement for the least operational overhead compared to fully managed services.

Therefore, an Amazon MemoryDB cluster in Multi-AZ mode provides the required ultra-low latency, rapid failover for write availability, durability for persistence, and least operational overhead.

This solution will meet the requirements of implementing a highly available architecture for the application servers in AWS. Creating a pool of ENIs will allow the application servers to have consistent MAC addresses, which are needed for the license files. Requesting license files from the vendor for the pool and storing them in Amazon S3 will ensure that the license files are available and secure. Creating a bootstrap automation script to download a license file and attach the corresponding ENI to an EC2 instance will automate the process of launching new application servers with valid licenses. Editing the bootstrap automation script to read the database server IP address from the AWS Systems Manager Parameter Store and inject the value into the local configuration files will enable the application servers to access the database without hard-coding the IP address in the configuration files. This will also allow changing the database server IP address without modifying the configuration files on each application server.

To ensure that game assets are fetched from the closest region and have a fallback option in case the assets become unavailable in the closest region, a solution architect should leverage Amazon CloudFront, a global content delivery network (CDN) service. By creating an Amazon CloudFront distribution and setting up origin groups, the architect can specify multiple origins (in this case, the Application Load Balancers in each region). The primary origin will serve content under normal circumstances, and if the content becomes unavailable, CloudFront will automatically switch to the secondary origin. This approach not only meets the requirement of regional proximity and redundancy but also optimizes latency and enhances the gaming experience by serving assets from the nearest geographical location to the end-user.

AWS Documentation on Amazon CloudFront and origin groups provides detailed instructions on setting up distributions with multiple origins for high availability and performance optimization. Additionally, AWS whitepapers and best practices on content delivery and global applications offer insights into effectively utilizing CloudFront and other AWS services to achieve low latency and high availability.

Configuring AWS CloudTrail to log S3 data events will enable logging all activities for objects in the S3 bucket1. Data events are object-level API operations such as GetObject, DeleteObject, and PutObject1. Configuring Amazon S3 to send object deletion events to an Amazon EventBridge event bus that publishes to an Amazon Simple Notification Service (Amazon SNS) topic will enable sending email notifications every time there is an attempt to delete data in the S3 bucket2. EventBridge can route events from S3 to SNS, which can send emails to subscribers2. Configuring a new S3 bucket to store the logs with an S3 Lifecycle policy will enable keeping the logs for 5 years in a cost-effective way3. A lifecycle policy can transition the logs to a cheaper storage class such as Glacier or delete them after a specified period of time3.

Step B is necessary so that the user in the destination account has the necessary permissions to access the source bucket and list its contents, read its objects. Step D is needed so that the user in the destination account has the necessary permissions to access the destination bucket and list contents, put objects, and set object ACLs Step F is necessary because the aws s3 sync command needs to be run using the IAM user credentials from the destination account, so that the objects will have the appropriate permissions for the user in the destination account once they are copied.

The company should use Amazon Aurora global database and Amazon DynamoDB global table to deploy the data tier components across two Regions. Amazon Aurora globaldatabase is a feature that allows a single Aurora database to span multiple AWS Regions, enabling low-latency global reads and fast recovery from Region-wide outages1. Amazon DynamoDB global table is a feature that allows a single DynamoDB table to span multiple AWS Regions, enabling low-latency global reads and writes and fast recovery from Region-wide outages2.

Migrating to an Amazon EKS cluster with managed node groups minimizes the effort required because:

EKS is fully managed, offering native Kubernetes support, making it easy to deploy the existing Kubernetes manifests without major changes.

Copying containers to Amazon ECR allows for fully managed, scalable container image storage in AWS, eliminating reliance on the on-premises container repository.

Deploying the existing manifests directly to EKS reuses all the existing configuration, such as service definitions, deployments, and scaling policies, simplifying migration.

Using Amazon Aurora PostgreSQL provides a fully managed, highly available database service that is compatible with PostgreSQL, reducing operational overhead compared to managing a self-hosted database.This approach leverages the power of AWS managed services while preserving the existing microservices and deployment practices, ensuring minimal disruption and fastest migration path.