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

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Question # 137

A company wants to use AWS to create a business continuity solution in case the company ' s main on-premises application fails. The application runs on physical servers that also run other applications. The on-premises application that the company is planning to migrate uses a MySQL database as a data store. All the company ' s on-premises applications use operating systems that are compatible with Amazon EC2.

Which solution will achieve the company ' s goal with the LEAST operational overhead?

Install the AWS Replication Agent on the source servers, including the MySQL servers. Set up replication for all servers. Launch test instances for regular drills. Cut over to the test instances to fail over the workload in the case of a failure event.

Install the AWS Replication Agent on the source servers, including the MySQL servers. Initialize AWS Elastic Disaster Recovery in the target AWS Region. Define the launch settings. Frequently perform failover and fallback from the most recent point in time.

Create AWS Database Migration Service (AWS DMS) replication servers and a target Amazon Aurora MySQL DB cluster to host the database. Create a DMS replication task to copy the existing data to the target DB cluster. Create a local AWS Schema Conversion Tool (AWS SCT) change data capture (CDC) task to keep the data synchronized. Install the rest of the software on EC2 instances by starting with a compatible base AMI.

Deploy an AWS Storage Gateway Volume Gateway on premises. Mount volumes on all on-premises servers. Install the application and the MySQL database on the new volumes. Take regular snapshots. Install all the software on EC2 Instances by starting with a compatible base AMI. Launch a Volume Gateway on an EC2 instance. Restore the volumes from the latest snapshot. Mount the new volumes on the EC2 instances in the case of a failure event.

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Question # 138

A company has an organization in AWS Organizations that includes a separate AWS account for each of the company ' s departments. Application teams from different

departments develop and deploy solutions independently.

The company wants to reduce compute costs and manage costs appropriately across departments. The company also wants to improve visibility into billing for individual departments. The company does not want to lose operational flexibility when the company selects compute resources.

Which solution will meet these requirements?

Use AWS Budgets for each department. Use Tag Editor to apply tags to appropriate resources. Purchase EC2 Instance Savings Plans.

Configure AWS Organizations to use consolidated billing. Implement a tagging strategy that identifies departments. Use SCPs to apply tags to appropriateresources. Purchase EC2 Instance Savings Plans.

Configure AWS Organizations to use consolidated billing. Implement a tagging strategy that identifies departments. Use Tag Editor to apply tags to appropriate resources. Purchase Compute Savings Plans.

Use AWS Budgets for each department. Use SCPs to apply tags to appropriate resources. Purchase Compute Savings Plans.

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Question # 139

A life sciences company is using a combination of open source tools to manage data analysis workflows and Docker containers running on servers in its on-premises data center to process genomics data Sequencing data is generated and stored on a local storage area network (SAN), and then the data is processed. The research and development teams are running into capacity issues and have decided to re-architect their genomics analysis platform on AWS to scale based on workload demands and reduce the turnaround time from weeks to days

The company has a high-speed AWS Direct Connect connection Sequencers will generate around 200 GB of data for each genome, and individual jobs can take several hours to process the data with ideal compute capacity. The end result will be stored in Amazon S3. The company is expecting 10-15 job requests each day

Which solution meets these requirements?

Use regularly scheduled AWS Snowball Edge devices to transfer the sequencing data into AWS When AWS receives the Snowball Edge device and the data is loaded into Amazon S3 use S3 events to trigger an AWS Lambda function to process the data

Use AWS Data Pipeline to transfer the sequencing data to Amazon S3 Use S3 events to trigger an Amazon EC2 Auto Scaling group to launch custom-AMI EC2 instances running the Docker containers to process the data

Use AWS DataSync to transfer the sequencing data to Amazon S3 Use S3 events to trigger an AWS Lambda function that starts an AWS Step Functions workflow Store the Docker images in Amazon Elastic Container Registry (Amazon ECR) and trigger AWS Batch to run the container and process the sequencing data

Use an AWS Storage Gateway file gateway to transfer the sequencing data to Amazon S3 Use S3 events to trigger an AWS Batch job that runs on Amazon EC2 instances running the Docker containers to process the data

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Question # 140

A company runs its application in the eu-west-1 Region and has one account for each of its environments development, testing, and production All the environments are running 24 hours a day 7 days a week by using stateful Amazon EC2 instances and Amazon RDS for MySQL databases The databases are between 500 GB and 800 GB in size

The development team and testing team work on business days during business hours, but the production environment operates 24 hours a day. 7 days a week. The company wants to reduce costs AH resources are tagged with an environment tag with either development, testing, or production as the key.

What should a solutions architect do to reduce costs with the LEAST operational effort?

Create an Amazon EventBridge (Amazon CloudWatch Events) rule that runs once every day Configure the rule to invoke one AWS Lambda function that starts or stops instances based on the tag day and time.

Create an Amazon EventBridge (Amazon CloudWatch Events) rule that runs every business day in the evening. Configure the rule to invoke an AWS Lambda function that stops instances based on the tag-Create a second EventBridge (CloudWatch Events) rule that runs every business day in the morning Configure the second rule to invoke another Lambda function that starts instances based on the tag

Create an Amazon EventBridge (Amazon CloudWatch Events) rule that runs every business day in the evening Configure the rule to invoke an AWS Lambda function that terminates instances based on the tag Create a second EventBridge (CloudWatch Events) rule that runs every business day in the morning Configure the second rule to invoke another Lambda function that restores the instances from their last backup based on the tag.

Create an Amazon EventBridge rule that runs every hour. Configure the rule to invoke one AWS Lambda function that terminates or restores instances from their last backup based on the tag. day, and time.

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Question # 141

A machine learning (ML) company continuously develops new versions of its models. The models are integrated into a web application that uses a RESTful API to call custom Python code to invoke the models. The application runs in an Amazon EC2 Auto Scaling group.

Customer feedback indicates that new model versions sometimes perform worse than previous versions. The company wants to implement a gradual deployment approach in which 10% of traffic is initially routed to new models to verify performance. The solution must also reduce operational overhead for infrastructure management.

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

Deploy model versions to an Amazon SageMaker AI multi-model endpoint. Configure the weight for the new version to 10% and the previous version to 90%. Migrate the Python code to an AWS Lambda function. Use Amazon API Gateway to invoke the Lambda function.

Deploy each model version to a separate Amazon SageMaker Serverless Inference endpoint. Configure an AWS Lambda function to route 10% of invocation calls to the new endpoint and 90% to the previous version based on weighted random selection. Use Amazon API Gateway to invoke the Lambda function.

Deploy each model version to a separate Amazon SageMaker AI single-model endpoint. Configure an AWS Lambda function to route 10% of invocation calls to the new endpoint ' s URL and 90% to the previous version. Use Amazon API Gateway to invoke the Lambda function.

Deploy model versions to an Amazon SageMaker Serverless Inference endpoint. Use Amazon API Gateway with a canary deployment to gradually shift 10% of traffic to the new model version while maintaining 90% of traffic on the previous version.

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Question # 142

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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Question # 143

A company has on-premises Linux, Windows, and Ubuntu servers that run many applications. The servers run on physical machines and VMs. The company plans to migrate the servers to Amazon EC2 instances.

The company needs to accomplish the following goals:

â€¢ Measure actual server usage, system performance, and running processes.

â€¢ List system configurations.

â€¢ Understand details of the network connections between systems.

â€¢ Analyze application components and dependencies within on-premises workloads.

â€¢ Receive EC2 instance sizing recommendations from AWS.

Which solution will meet these requirements?

Install AWS Systems Manager Agent (SSM Agent) on the physical machines and VMs to gather performance and usage information from servers. Use Systems Manager Application Manager to discover existing servers and to group servers into applications before the migration. Generate EC2 instance recommendations by using AWS Pricing Calculator.

Install the Amazon Inspector agent on the physical machines and VMs to gather performance and usage information from servers. Use AWS Migration Hub to discover existing servers and to group servers into applications before the migration. Generate EC2 instance recommendations by using AWS Compute Optimizer.

Install the AWS Application Discovery Agent on the physical machines and VMs to gather performance and usage information from servers. Use AWS Migration Hub to discover existing servers and to group servers into applications before the migration. Generate EC2 instance recommendations by using Migration Hub.

Install the unified Amazon CloudWatch agent on the physical machines and VMs to gather performance and usage information from servers. Use AWS Migration Hub to discover existing servers and to group servers into applications before the migration. Generate EC2 instance recommendations by using AWS Compute Optimizer.

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Answer:

Explanation:

For migration planning, AWS provides specific tooling to discover on-premises servers, collect detailed performance and configuration data, and analyze application dependencies. The primary service for this purpose is AWS Application Discovery Service, which integrates with AWS Migration Hub.

The AWS Application Discovery Agent is installed on on-premises physical servers and VMs. It collects detailed information such as CPU, memory, and disk utilization; processes and services running on the system; system configuration details; and network connections between systems. This allows the company to understand how servers communicate, what applications are running, and what dependencies exist between components.

This data is sent to AWS Application Discovery Service and surfaced through AWS Migration Hub, where the company can view discovered servers, group them logically into applications, and analyze migration readiness. Migration Hub can use this collected data to provide recommendations for EC2 instance sizing that are based on actual on-premises utilization metrics, helping choose appropriate EC2 instance types and sizes.

Option C directly describes installing the AWS Application Discovery Agent, using AWS Migration Hub to discover and group servers into applications, and using Migration Hub to generate EC2 instance recommendations. This aligns with AWSâ€™s migration assessment tooling and meets all the stated goals: performance measurement, system configuration listing, network connection understanding, dependency analysis, and instance sizing recommendations.

Option A uses AWS Systems Manager Agent and Application Manager. While Systems Manager can manage servers and collect some information, it is not the primary tool for detailed migration discovery, networking dependency mapping, and migration-focused analysis. It also suggests using AWS Pricing Calculator for instance recommendations, which requires manual input and does not automatically derive recommendations from observed utilization and dependencies.

Option B suggests using the Amazon Inspector agent to gather performance and usage information. Amazon Inspector is designed for vulnerability and security assessments, not for detailed migration discovery or dependency mapping. Additionally, AWS Compute Optimizer currently bases its recommendations on usage metrics from AWS resources, not on-premises servers.

Option D uses the CloudWatch agent to collect metrics and then Migration Hub and Compute Optimizer. The CloudWatch agent is not the standard agent for migration discovery and dependency analysis of on-premises workloads. Also, Compute Optimizer focuses on optimization of existing AWS resources rather than creating recommendations based on on-premises utilization.

Therefore, installing the AWS Application Discovery Agent and using AWS Migration Hub (option C) is the correct solution to satisfy all the requirements for migration assessment and EC2 sizing recommendations.

[References:AWS documentation on AWS Application Discovery Service and AWS Migration Hub for discovering on-premises servers, analyzing dependencies, and planning migrations.AWS migration guidance on using discovery data to generate EC2 instance sizing recommendations., , ]

Question # 144

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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