SAP-C02 Exam Dumps - AWS Certified Solutions Architect - Professional

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Question # 113

A company has a payment gateway that processes millions of daily transactions on AWS. The solution uses Amazon ECS with a single Amazon EC2 instance that is not configured for auto scaling and an Amazon Aurora PostgreSQL database. All the solution ' s resources are deployed in the same Availability Zone. The company uses Amazon Route 53 to manage its domain name resolution.

The company needs to implement a new strategy to make the application more highly available.

Which solution will meet this requirement with the LEAST operational overhead?

Set up an Amazon RDS Proxy in front of the Aurora database. Modify the Aurora database to a Multi-AZ DB cluster by adding a read replica in a second Availability Zone.

Configure Amazon ECS services to distribute tasks across multiple Availability Zones. Create a cross-Region read replica of the Aurora database in a second AWS Region. Create a script to perform a manual failover process.

Configure Amazon ECS services on AWS Fargate to distribute tasks across multiple Availability Zones. Modify the Aurora database to a Multi-AZ DB cluster by adding a read replica in a second Availability Zone.

Deploy the gateway application into a second AWS Region. Migrate the Aurora database to an Aurora global database. Configure Route 53 for active-active gateway request routing.

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Answer:

Explanation:

C is correct because it converts the singleâ€“Availability Zone, single-EC2-instance ECS design into a managed, multi-AZ, self-healing architecture with minimal day-to-day operations. The current design has multiple single points of failure: one EC2 instance for ECS capacity and one Availability Zone for all components. Moving the ECS service to AWS Fargate removes the need to manage EC2 instances (capacity provisioning, patching, and scaling of the container instances) and allows the service to run tasks across multiple Availability Zones for higher availability. On the database side, modifying Aurora PostgreSQL to a Multi-AZ DB cluster (by adding a replica in another AZ) increases availability and supports faster recovery from an AZ failure with AWS-managed failover.

Why the other options are less suitable:

A: Making Aurora Multi-AZ improves database availability, but it does not address the compute layerâ€™s biggest issue: ECS is on a single EC2 instance in one AZ with no auto scaling. RDS Proxy can help with connection management, but it does not fix the applicationâ€™s single-AZ ECS single-instance availability risk.

B: Cross-Region read replicas and manual failover scripts increase operational burden. Also, it keeps ECS on EC2 (still requires instance management) and introduces a manual failover process, which is the opposite of â€œleast operational overhead.â€

D: Multi-Region active-active plus Aurora global database can deliver very high availability, but it adds significant complexity (multi-Region deployment, routing strategy, global database considerations, operational procedures). That is higher operational overhead than a straightforward multi-AZ design using managed services.

[References:, Amazon ECS Documentation: service placement across multiple Availability Zones; high availability patterns for ECS services, AWS Fargate Documentation: serverless container compute that removes the need to manage EC2 instances and supports multi-AZ task placement, Amazon Aurora PostgreSQL Documentation: Multi-AZ Aurora architecture, replicas across Availability Zones, and managed failover behavior, AWS Well-Architected Framework (Reliability Pillar): eliminating single points of failure with multi-AZ architectures and using managed services to reduce operational burden, AWS Certified Solutions Architect â€“ Professional (SAP-C02) Exam Guide: designing highly available workloads, selecting managed services to reduce operational overhead, , , , ]

Question # 114

A company migrated to AWS and uses AWS Business Support. The company wants to monitor thecost-effectiveness of Amazon EC2 instances. The EC2 instances have tags for department, business unit, and environment. Development EC2 instances have high cost but low utilization.

The company needs to detect and stop any underutilized development EC2 instances. Instances are underutilized if they had 10% or less average CPU utilization and 5 MB or less network I/O for at least 4 of the past 14 days.

Which solution will meet these requirements with the LEAST operational overhead?

Configure Amazon CloudWatch dashboards to monitor EC2 instance utilization based on tags for department, business unit, and environment. Create an Amazon EventBridge rule that invokes an AWS Lambda function to stop underutilized development EC2 instances.

Configure AWS Systems Manager to track EC2 instance utilization and report underutilized instances to Amazon CloudWatch. Filter the CloudWatch data by tags for department, business unit, and environment. Create an Amazon EventBridge rule that invokes an AWS Lambda function to stop underutilized EC2 instances.

Create an Amazon EventBridge rule to detect low utilization of EC2 instances reported by AWS Trusted Advisor. Configure the rule to invoke a Lambda function that filters the data by tags for department, business unit, and environment and stops underutilized development EC2 instances.

Create an AWS Lambda function to run daily to retrieve utilization data for all EC2 instances. Save the data to an Amazon DynamoDB table. Create a QuickSight dashboard that uses the DynamoDB table as a data source to identify and stop underutilized development EC2 instances.

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Question # 115

A company creates an Amazon API Gateway API and shares the API with an external development team. The API uses AWS Lambda functions and is deployed to a stage that is named Production.

The external development team is the sole consumer of the API. The API experiences sudden increases of usage at specific times, leading to concerns about increased costs. The company needs to limit cost and usage without reworking the Lambda functions.

Which solution will meet these requirements MOST cost-effectivery?

Configure the API to send requests to Amazon SQS queues instead of directly to the Lambda functions. Update the Lambda functions to consume messages from the queues and to process the requests. Set up the queues to invoke the Lambda functions when new messages arrive.

Configure provisioned concurrency for each Lambda function. Use AWS Application Auto Scaling to register the Lambda functions as targets. Set up scaling schedules to increase and decrease capacity to match changes in API usage.

Create an API Gateway API key and an AWS WAF Regional web ACL. Associate the web ACL with the Production stage. Add a rate-based rule to the web ACL. In the rule, specify the rate limit and a custom request aggregation that uses the X-API-Key header. Share the API key with the external development team.

Create an API Gateway API key and usage plan. Define throttling limits and quotas in the usage plan. Associate the usage plan with the Production stage and the API key. Share the API key with the external development team.

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Question # 116

A company has implemented an ordering system using an event-driven architecture. During initial testing, the system stopped processing orders. Further log analysis revealed that one order message in an Amazon Simple Queue Service (Amazon SQS) standard queue was causing an error on the backend and blocking all subsequentorder messages The visibility timeout of the queue is set to 30 seconds, and the backend processing timeout is set to 10 seconds. A solutions architect needs to analyze faulty order messages and ensure that the system continues to process subsequent messages.

Which step should the solutions architect take to meet these requirements?

Increase the backend processing timeout to 30 seconds to match the visibility timeout.

Reduce the visibility timeout of the queue to automatically remove the faulty message.

Configure a new SQS FIFO queue as a dead-letter queue to isolate the faulty messages.

Configure a new SQS standard queue as a dead-letter queue to isolate the faulty messages.

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Question # 117

A company uses a software package for surveys. During surveys, data is uploaded from a field operator ' s device to an Amazon S3 bucket. A custom application that runs on several Amazon EC2 instances polls the S3 bucket for new data. When new data is available, the software processes the data.

The data uploads are infrequent. The processing software can take up to 25 minutes to analyze each data upload. The company wants to optimize the application workflow to process the S3 data.

Which solution will meet these requirements with the LEAST operational overhead?

Modify the application to accept new S3 object keys as inputs. Containerize the application. Deploy the container to an Amazon ECS cluster that uses the AWS Fargate launch type. Configure S3 bucket notifications to send events to Amazon EventBridge when new objects are uploaded. Create an EventBridge rule that invokes an ECS task to run the application when a new S3 object event occurs.

Modify the application to accept new S3 object keys as inputs. Containerize the application. Deploy the container image to AWS Lambda functions. Create a new AWS Step Functions state machine to invoke the Lambda functions. Configure the state machine with a Task state that calls the Lambda functions. Set the Task state ' s Timeout property to 30 minutes.

Modify the application to accept new S3 object keys as inputs. Move the application from EC2 instances to Amazon ECS by using the EC2 capacity provider. Create an AWS Glue crawler to check the S3 bucket and invoke the application. Configure the application to process the data when the data is uploaded to Amazon S3.

Modify the application to use HTTP to poll new S3 object keys that reference data to process. Containerize the application. Deploy the container image to AWS Lambda functions. Configure S3 bucket notifications to send events to Amazon EventBridge when new objects are uploaded. Create an EventBridge rule that invokes the Lambda functions to post the new objects to HTTP endpoints by using fan-out.

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Question # 118

A manufacturing company is building an inspection solution for its factory. The company has IPcameras at the end of each assembly line. The company has used Amazon SageMaker to train a machine learning (ML) model to identify common defects from still images.

The company wants to provide local feedback to factory workers when a defect is detected. The company must be able to provide this feedback even if the factoryâ€™s internet connectivity is down. The company has a local Linux server that hosts an API that provides local feedback to the workers.

How should the company deploy the ML model to meet these requirements?

Set up an Amazon Kinesis video stream from each IP camera to AWS. Use Amazon EC2 instances to take still images of the streams. Upload the images to an Amazon S3 bucket. Deploy a SageMaker endpoint with the ML model. Invoke an AWS Lambda function to call the inference endpoint when new images are uploaded. Configure the Lambda function to call the local API when a defect is detected.

Deploy AWS IoT Greengrass on the local server. Deploy the ML model to the Greengrass server. Create a Greengrass component to take still images from the cameras and run inference. Configure the component to call the local API when a defect is detected.

Order an AWS Snowball device. Deploy a SageMaker endpoint the ML model and an Amazon EC2 instance on the Snowball device. Take still images from the cameras. Run inference from the EC2 instance. Configure the instance to call the local API when a defect is detected.

Deploy Amazon Monitron devices on each IP camera. Deploy an Amazon Monitron Gateway on premises. Deploy the ML model to the Amazon Monitron devices. Use Amazon Monitron health state alarms to call the local API from an AWS Lambda function when a defect is detected.

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Question # 119

A company needs a highly available database solution for an application. The solution must be able to fail over to a secondary AWS Region with an RPO of 5 minutes and an RTO of 20 minutes. The database is approximately 10 TB in size.

Which solution will meet these requirements?

Deploy an Amazon Aurora DB cluster and take snapshots of the cluster every 5 minutes. When each snapshot is complete, copy the snapshot to a secondary Region.

Deploy an Amazon RDS Multi-AZ DB cluster with a cross-Region read replica in a secondary Region. Use an Amazon CloudWatch alarm to invoke an AWS Lambda function that promotes the read replica to become the primary in the event of a failure.

Deploy an Amazon Aurora DB cluster in the primary Region. Configure Amazon EventBridge to target Amazon RDS to create a second cluster in the event of a failure. Use AWS DMS to keep the secondary Region in sync with the primary Region.

Deploy an Amazon RDS Multi-AZ DB cluster in the primary Region with a cross-Region read replica in a secondary Region. Configure automated backups and enable automated failover to promote the read replica to become the primary in the secondary Region.

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Answer:

Explanation:

The requirements specify cross-Region failover with an RPO of 5 minutes and an RTO of 20 minutes for a large (10 TB) database. Achieving a low RPO in a secondary Region typically requires near-continuous replication, not periodic snapshots. Achieving a 20-minute RTO requires that the standby data store already exists in the secondary Region and can be promoted quickly.

A cross-Region read replica provides continuous asynchronous replication from the primary Region to a replica in the secondary Region. This is the standard managed approach for cross-Region disaster recovery for Amazon RDS engines that support read replicas. With a read replica already running in the secondary Region, failover can be accomplished by promoting the replica to a standalone primary database instance. This supports an RTO target like 20 minutes because the data is already present and the promotion operation is typically faster than restoring from a full snapshot, especially at 10 TB scale.

Option B describes creating an RDS Multi-AZ DB cluster in the primary Region and a cross-Region read replica in the secondary Region. Multi-AZ addresses high availability within the primary Region, while the cross-Region replica addresses disaster recovery. The option also includes automation using CloudWatch alarms and a Lambda function to promote the replica when a failure is detected. This supports the required RTO by reducing manual intervention time and supports the RPO by using continuous replication.

Option A is not practical for RPO 5 minutes at 10 TB. Taking and copying snapshots every 5 minutes would be operationally heavy and would not complete within the required window; snapshot creation and cross-Region copy are not designed for such high-frequency near-real-time DR. It also risks missing the RPO due to snapshot completion time.

Option C introduces unnecessary complexity and additional services. Creating a second cluster only at failure time is not compatible with a 20-minute RTO for a 10 TB database, and using DMS for continuous sync is more development and operational work than using native RDS replication. This is not the least overhead or most direct DR design.

Option D is incorrect as stated because cross-Region read replicas are not automatically failed over and promoted by an â€œautomated failoverâ€ feature in the way Multi-AZ failover works within a Region. Automated backups do not provide near-real-time cross-Region recovery to meet a 5-minute RPO. The usual approach for cross-Region is to create the replica and then promote it through a controlled process, which can be automated via monitoring and Lambda as in option B.

Therefore, using a cross-Region read replica with automation to promote it provides the necessary RPO/RTO with a managed service approach.

[References:AWS documentation on Amazon RDS cross-Region read replicas for disaster recovery and replica promotion procedures.AWS documentation on Multi-AZ deployments for high availability within a Region and combining Multi-AZ with cross-Region read replicas for DR.AWS best practices for meeting RPO/RTO using continuous replication rather than periodic snapshots for large databases., , , , , ]

Question # 120

A solutions architect needs to review the design of an Amazon EMR cluster that is using the EMR File System (EMRFS). The cluster performs tasks that are critical to business needs. The cluster is running Amazon EC2 On-Demand Instances at all times tor all task, primary, and core nodes. The EMR tasks run each morning, starting at 1 ;00 AM. and take 6 hours to finish running. The amount of time to complete the processing is not a priority because the data is not referenced until late in the day.

The solutions architect must review the architecture and suggest a solution to minimize the compute costs.

Which solution should the solutions architect recommend to meet these requirements?

Launch all task, primary, and core nodes on Spool Instances in an instance fleet. Terminate the cluster, including all instances, when the processing is completed.

Launch the primary and core nodes on On-Demand Instances. Launch the task nodes on Spot Instances in an instance fleet. Terminate the cluster, including all instances, when the processing is completed. Purchase Compute Savings Plans to cover the On-Demand Instance usage.

Continue to launch all nodes on On-Demand Instances. Terminate the cluster, including all instances, when the processing is completed. Purchase Compute Savings Plans to cover the On-Demand Instance usage

Launch the primary and core nodes on On-Demand Instances. Launch the task nodes on Spot Instances in an instance fleet. Terminate only the task node instances when the processing is completed. Purchase Compute Savings Plans to cover the On-Demand Instance usage.

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