素敵なDOP-C02認定資格試験問題集 &合格スムーズDOP-C02日本語版問題集 |効率的なDOP-C02出題内容

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>> DOP-C02認定資格試験問題集 <<

DOP-C02日本語版問題集、DOP-C02出題内容

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AWS認定DevOps Engineer -Professional（DOP -C02）認定試験は、DevOps環境で働いており、継続的な統合と配信のためにAWSサービスを使用した経験があるITプロフェッショナル向けに設計されています。この認定は、とりわけ、自動化、監視、セキュリティの実践などの分野での候補者の知識とスキルを検証します。

Amazon AWS Certified DevOps Engineer - Professional 認定 DOP-C02 試験問題 (Q78-Q83):

質問 # 78
A company needs to update its order processing application to improve resilience and availability. The application requires a stateful database and uses a single-node Amazon RDS DB instance to store customer orders and transaction history. A DevOps engineer must make the database highly available.
Which solution will meet this requirement?

A. Use the RDS DB instance as the source instance to create read replicas in multiple Availability Zones. Deploy an Application Load Balancer to distribute read traffic across the read replicas.
B. Migrate the database to Amazon DynamoDB global tables. Configure automatic failover between AWS Regions by using Amazon Route 53 health checks.
C. Modify the RDS DB instance to be a Multi-AZ deployment. Verify automatic failover to the standby instance if the primary instance becomes unavailable.
D. Migrate the database to Amazon EC2 instances in multiple Availability Zones. Use Amazon Elastic Block Store (Amazon EBS) Mult-Attach to connect all the instances to a single EBS volume.

正解：C

質問 # 79
A developer is maintaining a fleet of 50 Amazon EC2 Linux servers. The servers are part of an Amazon EC2 Auto Scaling group, and also use Elastic Load Balancing for load balancing.
Occasionally, some application servers are being terminated after failing ELB HTTP health checks. The developer would like to perform a root cause analysis on the issue, but before being able to access application logs, the server is terminated.
How can log collection be automated?

A. Use Auto Scaling lifecycle hooks to put instances in a Terminating:Wait state. Create an Amazon CloudWatch subscription filter for EC2 Instance Terminate Successful and trigger a CloudWatch agent that invokes a script to collect logs, push them to Amazon S3, and complete the lifecycle action once logs are collected.
B. Use Auto Scaling lifecycle hooks to put instances in a Terminating:Wait state. Create an Amazon EventBridge rule for EC2 Instance-terminate Lifecycle Action and trigger an AWS Lambda function that invokes an SSM Run Command script to collect logs, push them to Amazon S3, and complete the lifecycle action once logs are collected.
C. Use Auto Scaling lifecycle hooks to put instances in a Pending:Wait state. Create an Amazon CloudWatch alarm for EC2 Instance Terminate Successful and trigger an AWS Lambda function that invokes an SSM Run Command script to collect logs, push them to Amazon S3, and complete the lifecycle action once logs are collected.
D. Use Auto Scaling lifecycle hooks to put instances in a Terminating:Wait state. Create an AWS Config rule for EC2 Instance-terminate Lifecycle Action and trigger a step function that invokes a script to collect logs, push them to Amazon S3, and complete the lifecycle action once logs are collected.

正解：B

解説：
Explanation
https://blog.fourninecloud.com/auto-scaling-lifecycle-hooks-to-export-server-logs-when-instance-terminating-58

質問 # 80
An ecommerce company has chosen AWS to host its new platform. The company's DevOps team has started building an AWS Control Tower landing zone. The DevOps team has set the identity store within AWS IAM Identity Center (AWS Single Sign-On) to external identity provider (IdP) and has configured SAML 2.0.
The DevOps team wants a robust permission model that applies the principle of least privilege. The model must allow the team to build and manage only the team's own resources.
Which combination of steps will meet these requirements? (Choose three.)

A. Enable attributes for access control in IAM Identity Center. Map attributes from the IdP as key-value pairs.
B. Create permission sets. Attach an inline policy that includes the required permissions and uses the aws:
PrincipalTag condition key to scope the permissions.
C. Enable attributes for access control in IAM Identity Center. Apply tags to users. Map the tags as key- value pairs.
D. Create IAM policies that include the required permissions. Include the aws:PrincipalTag condition key.
E. Create a group in the IdP. Place users in the group. Assign the group to accounts and the permission sets in IAM Identity Center.
F. Create a group in the IdP. Place users in the group. Assign the group to OUs and IAM policies.

正解：A、B、E

解説：
Using the principalTag in the Permission Set inline policy a logged in user belonging to a specific AD group in the IDP can be permitted access to perform operations on certain resources if their group matches the group used in the PrincipleTag. Basically you are narrowing the scope of privileges assigned via Permission policies conditionally based on whether the logged in user belongs to a specific AD Group in IDP. The mapping of the AD group to the request attributes can be doneusing SSO attributes where we can pass other attributes like the SAML token as well.
https://docs.aws.amazon.com/singlesignon/latest/userguide/abac.html

質問 # 81
A company is adopting AWS CodeDeploy to automate its application deployments for a Java-Apache Tomcat application with an Apache Webserver. The development team started with a proof of concept, created a deployment group for a developer environment, and performed functional tests within the application. After completion, the team will create additional deployment groups for staging and production.
The current log level is configured within the Apache settings, but the team wants to change this configuration dynamically when the deployment occurs, so that they can set different log level configurations depending on the deployment group without having a different application revision for each group.
How can these requirements be met with the LEAST management overhead and without requiring different script versions for each deployment group?

A. Create a script that uses the CodeDeploy environment variable DEPLOYMENT_GROUP_ID to identify which deployment group the instance is part of to configure the log level settings. Reference this script as part of the Install lifecycle hook in the appspec.yml file.
B. Create a CodeDeploy custom environment variable for each environment. Then place a script into the application revision that checks this environment variable to identify which deployment group the instance is part of. Use this information to configure the log level settings. Reference this script as part of the ValidateService lifecycle hook in the appspec.yml file.
C. Create a script that uses the CodeDeploy environment variable DEPLOYMENT_GROUP_ NAME to identify which deployment group the instance is part of. Use this information to configure the log level settings. Reference this script as part of the BeforeInstall lifecycle hook in the appspec.yml file.
D. Tag the Amazon EC2 instances depending on the deployment group. Then place a script into the application revision that calls the metadata service and the EC2 API to identify which deployment group the instance is part of. Use this information to configure the log level settings. Reference the script as part of the AfterInstall lifecycle hook in the appspec.yml file.

正解：C

解説：
Explanation
The following are the steps that the company can take to change the log level dynamically when the deployment occurs:
Create a script that uses the CodeDeploy environment variable DEPLOYMENT_GROUP_NAME to identify which deployment group the instance is part of.
Use this information to configure the log level settings.
Reference this script as part of the BeforeInstall lifecycle hook in the appspec.yml file.
The DEPLOYMENT_GROUP_NAME environment variable is automatically set by CodeDeploy when the deployment is triggered. This means that the script does not need to call the metadata service or the EC2 API to identify the deployment group.
This solution is the least complex and requires the least management overhead. It also does not require different script versions for each deployment group.
The following are the reasons why the other options are not correct:
Option A is incorrect because it would require tagging the Amazon EC2 instances, which would be a manual and time-consuming process.
Option C is incorrect because it would require creating a custom environment variable for each environment. This would be a complex and error-prone process.
Option D is incorrect because it would use the DEPLOYMENT_GROUP_ID environment variable.
However, this variable is not automatically set by CodeDeploy, so the script would need to call the metadata service or the EC2 API to get the deployment group ID. This would add complexity and overhead to the solution.

質問 # 82
A company's application uses a fleet of Amazon EC2 On-Demand Instances to analyze and process data. The EC2 instances are in an Auto Scaling group. The Auto Scaling group is a target group for an Application Load Balancer (ALB). The application analyzes critical data that cannot tolerate interruption. The application also analyzes noncritical data that can withstand interruption.
The critical data analysis requires quick scalability in response to real-time application demand. The noncritical data analysis involves memory consumption. A DevOps engineer must implement a solution that reduces scale-out latency for the critical data. The solution also must process the noncritical data.
Which combination of steps will meet these requirements? (Select TWO.)

A. For the noncritical data, create a second Auto Scaling group that uses a launch template. Configure the launch template to install the unified Amazon CloudWatch agent and to configure the CloudWatch agent with a custom memory utilization metric. Use Spot Instances. Add the new Auto Scaling group as the target group for the ALB. Modify the application to use two target groups for critical data and noncritical data.
B. For the critical data, modify the existing Auto Scaling group. Create a warm pool instance in the stopped state. Define the warm pool size. Create a new version of the launch template that has detailed monitoring enabled. Use On-Demand Instances.
C. For the critical data. modify the existing Auto Scaling group. Create a lifecycle hook to ensure that bootstrap scripts are completed successfully. Ensure that the application on the instances is ready to accept traffic before the instances are registered. Create a new version of the launch template that has detailed monitoring enabled.
D. For the noncritical data, create a second Auto Scaling group. Choose the predefined memory utilization metric type for the target tracking scaling policy. Use Spot Instances. Add the new Auto Scaling group as the target group for the ALB. Modify the application to use two target groups for critical data and noncritical data.
E. For the critical data, modify the existing Auto Scaling group. Create a warm pool instance in the stopped state. Define the warm pool size. Create a new version of the launch template that has detailed monitoring enabled. use Spot Instances.

正解：A、B

解説：
Explanation
For the critical data, using a warm pool1 can reduce the scale-out latency by having pre-initialized EC2 instances ready to serve the application traffic. Using On-Demand Instances can ensure that the instances are always available and not interrupted by Spot interruptions2.
For the noncritical data, using a second Auto Scaling group with Spot Instances can reduce the cost and leverage the unused capacity of EC23. Using a launch template with the CloudWatch agent4 can enable the collection of memory utilization metrics, which can be used to scale the group based on the memory demand. Adding the second group as a target group for the ALB and modifying the application to use two target groups can enable routing the traffic based on the data type.
References: 1: Warm pools for Amazon EC2 Auto Scaling 2: Amazon EC2 On-Demand Capacity Reservations 3: Amazon EC2 Spot Instances 4: Metrics collected by the CloudWatch agent

質問 # 83
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