Free Amazon Web Services SAP-C02 Practice Exam with Questions & Answers | Set: 2

This option allows the company to use Amazon CloudFront to improve the latency and availability of the API requests by caching the responses at the edge locations closest to the clients1. By enabling caching in the production stage, the company can reduce the number of calls made to the backend services, such as Lambda functions and Aurora Serverless DB cluster, and save on costs and resources2. This option also helps to handle traffic spikes and reduce database memory errors by serving cached responses instead of querying the database repeatedly.

Choosing an API endpoint type

Enabling API caching to enhance responsiveness

The company has migrated an application to EC2 and will deploy it in an additional Region. The usage is expected to be stable for 3 years, but parts of the application will be redesigned over the next 2 years to use AWS Lambda. Therefore, the company will gradually shift a portion of its compute usage from EC2 to Lambda.

Savings Plans are a flexible pricing model that provide lower prices in exchange for a commitment to a consistent amount of compute usage (measured in dollars per hour) for a 1- or 3-year term. There are two main types of Savings Plans:

• Compute Savings Plans: Apply to any EC2 instance usage regardless of Region, instance family, OS, tenancy, and also apply to AWS Fargate and AWS Lambda usage.

• EC2 Instance Savings Plans: Apply only to a specific instance family in a specific Region.

In this scenario, because there will be a gradual migration from EC2-based compute to Lambda-based compute, a plan that only applies to EC2 (EC2 Instance Savings Plan) risks underutilization as more usage moves to Lambda. Compute Savings Plans, by contrast, can cover EC2 now and Lambda later, maintaining high utilization of the commitment and maximizing effective discount.

Option A recommends evaluating Savings Plans recommendations each year in AWS Cost Management and purchasing a 1-year Compute Savings Plan based on those recommendations. The Cost Management tools and Cost Explorer provide Savings Plans recommendations based on actual usage patterns. Buying 1-year Compute Savings Plans and re-evaluating annually allows the company to adjust its committed spend each year as portions of the application move from EC2 to Lambda. This approach maximizes the chance that the Savings Plans coverage matches the actual combined EC2 and Lambda usage over time and avoids being locked into an EC2-only commitment that becomes increasingly underutilized.

Option B is not optimal because it recommends a 3-year EC2 Instance Savings Plan. That commitment applies only to specific EC2 instance families in a Region and does not apply to Lambda. As the company migrates portions of its workloads to Lambda, the EC2 Instance Savings Plan may become underutilized, reducing the effective discount and potentially increasing overall cost. Compute Optimizer also does not provide Savings Plans purchase recommendations; Savings Plans recommendations come from Cost Management and Cost Explorer.

Option C uses 1-year EC2 Instance Savings Plans and adjusts the commitment each year. While this gives some flexibility, EC2 Instance Savings Plans still do not apply to Lambda usage, so over time there is a risk of underutilizing the EC2 commitment as the Lambda portion grows. This does not maximize savings relative to Compute Savings Plans, which can cover both services.

Option D proposes a 3-year EC2 Instance Savings Plan and suggests decreasing the hourly commitment during the term as workloads move to Lambda. However, Savings Plans commitments cannot be decreased during the term. The company would remain obligated to the original 3-year commitment even if EC2 usage drops, leading to wasted commitment.

Therefore, purchasing 1-year Compute Savings Plans each year based on Savings Plans recommendations from AWS Cost Management (option A) is the best strategy to maximize cost savings while the company transitions part of the workload from EC2 to Lambda.

https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/HowItWorks.ReadWriteCapacityMode.html#HowItWorks.ProvisionedThroughput.Manual

https://aws.amazon.com/dms/schema-conversion-tool/

AWS Schema Conversion Tool (SCT) can automatically convert the database schema from Microsoft SQL Server to Amazon RDS for MySQL. This allows for a smooth transition of the database schema without any manual intervention. AWS DMS can then be used to migrate the data from the on-premises databases to the newly created Amazon RDS for MySQL instance. This service can perform a one-time migration of the data or can set up ongoing replication of data changes to keep the on-premises and AWS databases in sync.

https://aws.amazon.com/blogs/architecture/accelerating-your-migration-to-aws/ Build a business case with AWS Migration Evaluator The foundation for a successful migration starts with a defined business objective (for example, growth or new offerings). In order to enable the business drivers, the established business case must then be aligned to a technical capability (increased security and elasticity). AWS Migration Evaluator (formerly known as TSO Logic) can help you meet these objectives. To get started, you can choose to upload exports from third-party tools such as Configuration Management Database (CMDB) or install a collector agent to monitor. You will receive an assessment after data collection, which includes a projected cost estimate and savings of running your on-premises workloads in the AWS Cloud. This estimate will provide a summary of the projected costs to re-host on AWS based on usage patterns. It will show the breakdown of costs by infrastructure and software licenses. With this information, you can make the business case and plan next steps.

Create VPC Endpoint Service:

In the shared VPC, create a VPC endpoint service using the Network Load Balancer (NLB) that fronts the centralized application.

Enable the option to require endpoint acceptance to control which business unit VPCs can connect to the service.

Set Up VPC Endpoints in Business Unit VPCs:

In each business unit VPC, create a VPC endpoint that points to the VPC endpoint service created in the shared VPC.

Use the service name of the endpoint service created in the shared VPC for configuration.

Accept Endpoint Requests:

From the VPC endpoint service console in the shared VPC, review and accept endpoint connection requests from authorized business unit VPCs. This ensures that only authorized VPCs can access the centralized application.

Configure Routing:

Update the route tables in each business unit VPC to direct traffic destined for the centralized application through the VPC endpoint.

This solution ensures secure, private connectivity between the business unit VPCs and the shared VPC, even if there are overlapping CIDR blocks. It leverages AWS PrivateLink and VPC endpoints to provide scalable and controlled access​(AWS Documentation)​​(Amazon Web Services, Inc.)​.

https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/manage-cost-categories.html

https://aws.amazon.com/premiumsupport/knowledge-center/cost-explorer-analyze-spending-and-usage/

https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/manage-cost-categories.htmlhttps://docs.aws.amazon.com/cost-management/latest/userguide/ce-enable.html

The best combination of actions to meet the company’s requirements is Options A, C, and F.

Option A involves activating the user-defined cost allocation tags that represent the application and the team. This will allow the company to assign costs to different applications or teams, and will allow them to be tracked in the monthly AWS bill.

Option C involves creating a cost category for each application in Billing and Cost Management. This will allow the company to easily identify and compare costs across different applications and teams.

Option F involves enabling Cost Explorer. This will allow the company to view the costs of their AWS resources over the last 12 months and to create forecasts for the next 12 months.

These recommendations are in line with the official Amazon Textbook and Resources for the AWS Certified Solutions Architect - Professional certification. In particular, the book states that “You can use cost allocation tags to group your costs by application, team, or other categories” (Source:https://d1.awsstatic.com/training-and-certification/docs-sa-pro/AWS_Certified_Solutions_Architect_Professional_Exam_Guide_EN_v1.5.pdf).Additionally, the book states that “Cost Explorer enables you to view the costs of your AWS resources over the last 12 months and to create forecasts for the next 12 months” (Source:https://d1.awsstatic.com/training-and-certification/docs-sa-pro/AWS_Certified_Solutions_Architect_Professional_Exam_Guide_EN_v1.5.pdf).

To effectively gather information about network dependencies between VMs in an on-premises data center for migration to AWS, it ' s crucial to use tools that can capture detailed application and server dependencies. The AWS Application Discovery Service is designed for this purpose, particularly when migrating from environments like Linux-based VMware VMs. By installing the AWS Application Discovery Agent on the on-premises servers, the service can collect necessary data such as host IP addresses, hostnames, and network connection information. This data is crucial for creating a comprehensive network diagram that outlines the interactions and dependencies between various components of the on-premises infrastructure. The integration with AWS Migration Hub enhances this process by allowing the visualization of these dependencies in a network diagram format, aiding in the planning and execution of the migration process. This approach ensures a thorough understanding of the on-premises environment, which is essential for a successful migration to AWS.

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

" An error occurred (AccessDenied) when calling the PutObject operation: Access Denied " This error message indicates that your IAM user or role needs permission for the kms:GenerateDataKey action.

https://aws.amazon.com/blogs/architecture/create-dynamic-contact-forms-for-s3-static-websites-using-aws-lambda-amazon-api-gateway-and-amazon-ses/

B is correct because the problem is caused by inconsistent query-string casing leading to CloudFront cache misses, and the best place to normalize the request is at the CloudFront edge before cache-key evaluation. CloudFront Functions run at the edge with very low latency and are intended for lightweight request manipulations (for example, normalizing URLs, headers, and query strings) on viewer request events. By sorting query parameters and converting them to lowercase on the viewer request, CloudFront will treat semantically equivalent requests as the same cache key, increasing cache hit ratio and reducing the number of origin calls to API Gateway and downstream Lambda invocations. This directly reduces latency for global users.

Why the other options are less suitable:

A (Lambda@Edge on origin request): Lambda@Edge can also modify requests, but it is heavier-weight than CloudFront Functions for simple normalization logic. Also, doing this on origin request occurs after CloudFront has already decided whether there is a cache hit. If the cache key is still based on the original mixed-case query string at the viewer side, you won’t consistently prevent cache misses. Normalization must happen before cache lookup to maximize cache hits.

C (API Gateway mapping templates): This normalizes the request only after it has already missed in CloudFront and reached the origin. It does not fix CloudFront cache fragmentation, so it does not meet the “minimal latency” goal as effectively.

D (Lambda authorizer): Lambda authorizers are for authorization/authentication decisions. Using an authorizer for query normalization adds unnecessary overhead and still happens at API Gateway (origin), not at CloudFront before cache evaluation.

The best solution is to deploy an AWS Lambda function to add capacity to the Amazon Redshift cluster by using an elastic resize operation when the cluster’s CPU metrics in Amazon CloudWatch reach 80%. This solution will enable the cluster to scale up or down quickly by adding or removing nodes within minutes. This will improve the performance of the complex read queries and also reduce the cost by scaling down when the demand decreases. This solution is more cost-effective than using a classic resize operation, which takes longer and requires more downtime. It is also more suitable than using Amazon EMR, which is designed for big data processing rather than data warehousing. References: Amazon Redshift Documentation, Resizing clusters in Amazon Redshift, [Amazon EMR Documentation]

Updating the ingestion process to use Amazon Kinesis Data Firehose to save data to Amazon S3 will reduce the need for EC2 instances and EBS volumes for data storage1. Using AWS Batch with Spot Instances to perform nightly processing will leverage the cost savings of Spot Instances, which are up to 90% cheaper than On-Demand Instances2. AWS Batch will also handle the scheduling and scaling of the processing jobs. Setting the maximum Spot price to 50% of the On-Demand price will reduce the chances of interruption and ensure that the processing is cost-effective.

Set Up AWS Elastic Disaster Recovery:

Navigate to the AWS Elastic Disaster Recovery (DRS) console.

Configure the Elastic Disaster Recovery service to replicate your on-premises VMware vSphere VMs to Amazon EC2 instances. This involves installing the AWS Replication Agent on your VMs.

Configure Replication Settings:

Define the replication settings, including the Amazon EC2 instance type and the Amazon EBS volume configuration. Ensure that the replication frequency meets your Recovery Point Objective (RPO) of 5 minutes.

Monitor Data Replication:

Monitor the initial data replication process in the Elastic Disaster Recovery console. Once the initial sync is complete, the status should show as " Healthy " indicating that the data replication is up-to-date and within the RPO requirements.

Disaster Recovery (Failover):

In the event of a disaster, initiate a failover from the Elastic Disaster Recovery console. This will launch the replicated Amazon EC2 instances using the Amazon EBS volumes with the latest data.

Failback Process:

Once the on-premises environment is restored, perform a failback operation to synchronize the data from AWS back to your on-premises VMware environment. Use the failback client provided by AWS Elastic Disaster Recovery to ensure data consistency and minimal downtime during the failback process.

Using AWS Elastic Disaster Recovery provides a low-overhead, automated solution for disaster recovery that ensures minimal data loss and meets the RPO requirement of 5 minutes​(Amazon Web Services, Inc.)​​(AWS Documentation)​.