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

https://aws.amazon.com/getting-started/hands-on/move-to-managed/migrate-mongodb-to-documentdb/

The goal is to ensure the application operates across multiple Availability Zones. That means removing single points of failure in compute, database, and network egress for private subnets.

For compute, the Auto Scaling group currently has min=1 and max=1, which guarantees only one instance is running at a time. Even if the Auto Scaling group spans multiple subnets, a single-instance configuration cannot provide multi-AZ active capacity because a failure of the Availability Zone hosting the single instance would cause downtime until a new instance is launched in a different zone. To operate across multiple Availability Zones, the solution should run multiple instances across different AZs, which implies increasing the minimum capacity above 1 and allowing the group to launch across multiple subnets/AZs.

For the database, a single-AZ RDS for MySQL instance is a single point of failure. Converting to an RDS Multi-AZ configuration provides synchronous replication to a standby in a different Availability Zone and managed failover to maintain availability when the primary AZ or instance fails.

For NAT, a single NAT gateway is an AZ-scoped managed resource. If private subnets in other AZs route to a NAT gateway in one AZ, an AZ outage can break outbound connectivity for workloads that depend on NAT. The resilient pattern is to deploy a NAT gateway in each Availability Zone and configure each private subnet’s route table to use the NAT gateway in the same AZ.

Option A addresses all three: it deploys additional NAT gateways per AZ and updates route tables accordingly, converts RDS to Multi-AZ, and adjusts Auto Scaling to launch across AZs with min and max set to 3 so there are instances running in multiple AZs simultaneously. This ensures the application remains available across AZ failures, meeting the requirement.

Option B replaces NAT with a virtual private gateway, which is used for VPN/Direct Connect connectivity, not for internet egress from private subnets. It does not satisfy the NAT functionality requirement. Although Aurora can provide high availability, the NAT replacement is not correct for the described architecture.

Option C increases operational overhead by replacing NAT gateway with NAT instances, which are self-managed and less resilient without additional design. It also changes the database engine to PostgreSQL unnecessarily and does not directly address the requirement with minimal change.

Option D improves NAT resiliency but only enables RDS automatic backups, which is a durability feature, not a high availability feature. Backups do not provide automatic failover and do not ensure the application will operate across multiple AZs. Also, keeping Auto Scaling min/max at 1 still leaves the application compute layer single-AZ at any given moment.

Therefore, option A is the correct solution.

Comprehensive and Detailed Explanation From Exact Extract:

C is correct because the root cause is synchronous chaining of Lambda functions that causes the initial function to wait and time out. AWS Step Functions is designed to orchestrate multiple steps (including Lambda invocations) with managed state, retries, and timeouts at the workflow level. By moving the chaining logic into a Step Functions state machine and removing direct function-to-function invocations, the initial Lambda no longer needs to remain running while waiting for downstream functions. Step Functions can coordinate the sequence (or parallel branches), handle transient failures with retries/backoff, and maintain execution state without requiring a long-lived “waiting” Lambda invocation. This resolves timeouts while preserving the operational and cost advantages of a serverless Lambda-based architecture and typically requires minimal code change compared to rewriting or migrating platforms.

Why the other options are incorrect:

A: Migrating to containers/EKS is a major architectural change and increases operational overhead, violating “minimum application changes” and the desire to keep Lambda benefits.

B: Rewriting in Java is extensive and unnecessary. Adding SQS between functions changes invocation style and error handling and still requires significant redesign (and does not inherently solve orchestration/branching as cleanly as Step Functions).

D: Grouping functions into containers and running on ECS Spot changes the compute model and introduces more operational overhead and larger application changes than adopting Step Functions.

CodeCommit may use S3 on the back end (and it also uses DynamoDB on the back end) but I don't think they're stored in buckets that you can see or point Macie to. In fact, there are even solutions out there describing how to copy your repo from CodeCommit into S3 to back it up:https://docs.aws.amazon.com/prescriptive-guidance/latest/patterns/automate-event-driven-backups-from-codecommit-to-amazon-s3-using-codebuild-and-cloudwatch-events.html

https://docs.aws.amazon.com/compute-optimizer/latest/APIReference/API_ExportLambdaFunctionRecommendations.html

The company’s goal is to modernize the application while minimizing operational overhead. The current architecture relies heavily on self-managed EC2 instances for web serving, APIs, search, and relational data storage. This increases operational burden related to patching, scaling, availability, and troubleshooting.

Option B replaces self-managed components with fully managed AWS services that are purpose-built for each workload type. Hosting the static web application on Amazon S3 with Amazon CloudFront removes the need to manage web servers such as NGINX and provides global content delivery with built-in scalability and high availability. Migrating the search data to Amazon OpenSearch Service eliminates the need to manage an EC2-based OpenSearch node, providing a managed search service with built-in scaling, patching, and monitoring. Migrating PostgreSQL to Amazon Aurora PostgreSQL removes the operational overhead of managing database instances on EC2 and provides managed backups, high availability, and performance optimizations. Running APIs on AWS App Runner further reduces operational effort by abstracting infrastructure management, scaling, and deployment, allowing the team to focus on application code.

Option A still requires running and operating containerized databases and search engines, which is not recommended and does not significantly reduce operational overhead compared to EC2-based deployments. Databases and OpenSearch are better consumed as managed services rather than as containers.

Option C introduces Amazon EKS, which adds significant operational complexity. Even with managed node groups, Kubernetes requires cluster management, networking configuration, upgrades, and ongoing operational expertise. This contradicts the requirement for the least operational overhead.

Option D attempts to run databases and OpenSearch on AWS App Runner. App Runner is designed for stateless web services and APIs, not for stateful services such as databases or search engines. This architecture is not aligned with AWS best practices and would introduce reliability and operational challenges.

Therefore, using managed services such as Amazon CloudFront, Amazon S3, Amazon OpenSearch Service, Amazon Aurora PostgreSQL, and AWS App Runner provides the most modern architecture with the least operational overhead.

(https://stackoverflow.com/questions/50250114/athena-vs-redshift-spectrum)

https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/placement-groups.html#placement- groups-cluster

https://aws.amazon.com/premiumsupport/knowledge-center/cross-account-access-denied-error-s3/

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.

The company should create a second target group that is referenced by the ALB. The company should deploy the new logic to EC2 instances in this new target group. The company should update the ALB listener rule to use weighted target groups. The company should configure ALB target group stickiness. This solution will meet the requirements because weighted target groups are a feature that enables you to distribute traffic across multiple target groups using a single listener rule. You can specify a weight for each target group, which determines the percentage of requests that are routed to that target group. For example, if you specify two target groups, each with a weight of 10, each target group receives half the requests1. By creating a second target group and deploying the new logic to EC2 instances in this new target group, the company can have two versions of its business logic running in parallel. By updating the ALB listener rule to use weighted target groups,the company can control how much traffic is sent to each version. By configuring ALB target group stickiness, the company can ensure that a customer uses the same version of the business logic during the testing window. Target group stickiness is a feature that enables you to bind a user’s session to a specific target within a target group for the duration of the session2.

The other options are not correct because:

Creating a second ALB and deploying the new logic to a set of EC2 instances in a new Auto Scaling group would not be as cost-effective or simple as using weighted target groups. A second ALB would incur additional charges and require more configuration and management. Updating the Route 53 record to use weighted routing would not ensure that a customer uses the same version of the business logic during the testing window, as DNS caching could affect how requests are routed.

Creating a new launch configuration for the Auto Scaling group and replacing it on the Auto Scaling group would not allow for gradual traffic shifting between versions. A launch configuration is a template that an Auto Scaling group uses to launch EC2 instances. You can specify information such as the AMI ID, instance type, key pair, security groups, and block device mapping for your instances3. However, replacing the launch configuration on an Auto Scaling group would affect all instances in that group, not just 10% of customers.

Creating a second Auto Scaling group and changing the ALB routing algorithm to least outstanding requests (LOR) would not allow for controlled traffic shifting between versions. A second Auto Scaling group would require more configuration and management. The LOR routing algorithm is a feature that enables you to route traffic based on how quickly targets respond to requests. The load balancer selects a target from the target group with the fewest outstanding requests4. However, this algorithm does not take into account customer sessions or weights.