Amazon Web Services Updated SAP-C02 Exam Questions and Answers by hiba

Amazon SageMaker is a fully managed machine learning service that allows users to build, train, and deploy machine learning models quickly and easily1. Users can export their existing TensorFlow models and store the model artifacts in Amazon S3, a highly scalable and durable object storage service2. Users can then deploy the model to Amazon SageMaker and create an endpoint that can be invoked by the web application to provide recommendations3. This way, the solution can leverage the AI/ML capabilities of Amazon SageMaker without having to rewrite the personalization model.

AWS Elastic Beanstalk is a service that allows users to deploy and manage web applications without worrying about the infrastructure that runs those applications. Users can host their Java application in AWS Elastic Beanstalk and configure it to communicate with the Amazon SageMaker endpoint. This way, the solution can reduce the operational overhead of managing servers, load balancers, scaling, and application health monitoring.

AWS Database Migration Service (AWS DMS) is a service that helps users migrate databases to AWS quickly and securely. Users can use AWS DMS to migrate their SQL Server database to Amazon RDS for SQL Server, a fully managed relational database service that offers high availability, scalability, security, and compatibility. This way, the solution canreduce the operational overhead of managing database servers, backups, patches, and upgrades.

Option A is incorrect because using AWS Server Migration Service (AWS SMS) to migrate the on-premises physical application server and the web application VMs to AWS is not cost-effective or scalable. AWS SMS is a service that helps users migrate on-premises workloads to AWS. However, for this use case, migrating the physical application server and the web application VMs to AWS will not take advantage of the AI/ML capabilities of Amazon SageMaker or the managed services of AWS Elastic Beanstalk and Amazon RDS.

Option C is incorrect because using AWS Application Migration Service to migrate the on-premises personalization model and VMs to Amazon EC2 instances in Auto Scaling groups is not cost-effective or scalable. AWS Application Migration Service is a service that helps users migrate applications from on-premises or other clouds to AWS without making any changes to their applications. However, for this use case, migrating the personalization model and VMs to EC2 instances will not take advantage of the AI/ML capabilities of Amazon SageMaker or the managed services of AWS Elastic Beanstalk and Amazon RDS.

Option D is incorrect because containerizing the personalization model and the Java application and using Amazon Elastic Kubernetes Service (Amazon EKS) managed node groups to deploy them to Amazon EKS is not necessary or cost-effective. Amazon EKS is a service that allows users to run Kubernetes on AWS without needing to install, operate, and maintain their own Kubernetes control plane or nodes. However, for this use case, containerizing and deploying the personalization model and the Java application will not take advantage of the AI/ML capabilities of Amazon SageMaker or the managed services of AWS Elastic Beanstalk. Moreover, using S3 Glacier Deep Archive as a storage class for images will incur a high retrieval fee and latency for accessing them.

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.

AWS Cost Anomaly Detection is a feature of the AWS Cost Management suite that leverages machine learning to enable continuous monitoring of your AWS costs and usage, allowing you to identify unexpected and abnormal spending1. You can create cost monitors that evaluate specific AWS services, member accounts, cost allocation tags, orcost categories based on your AWS account structure2. You can also configure alert subscriptions that notify you when a cost monitor detects an anomaly that meets your threshold2. In this case, you can create a cost monitor with a monitor type of AWS Service and apply a filter of Amazon EC2 to track the EC2 usage as a metric. You can then configure an alert subscription to notify the architecture team if the usage is 10% more than the average usage for the last 30 days, which is the anomaly detection period used by AWS Cost Anomaly Detection3.

This solution utilizes Amazon S3 and CloudFront to deploy the UI as a static website, which can be done with minimal development effort. The business logic and API tier can be containerized in a Docker image and stored in Amazon Elastic Container Registry (ECR) and run on Amazon Elastic Container Service (ECS) with the Fargate launch type, which allows the application to be highly available with minimal operational overhead. The data layer can be deployed on an Amazon Aurora MySQL DB cluster which is a fully managed relational database service.

Amazon Aurora provides high availability and performance for the data layer without the need for managing the underlying infrastructure.