DOP-C02 Prüfungsfrage & DOP-C02 Lernressourcen

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Die Amazon DOP-C02-Zertifizierung ist bei Arbeitgebern sehr geschätzt und kann Kandidaten helfen, sich auf einem wettbewerbsintensiven Arbeitsmarkt abzuheben. Durch den Erwerb dieser Zertifizierung zeigen DevOps-Profis ihre Fähigkeit, hoch skalierbare, fehlertolerante und sichere Anwendungen auf der AWS-Plattform zu entwerfen, bereitzustellen und zu verwalten. Darüber hinaus demonstriert die Zertifizierung ein Engagement für kontinuierliches Lernen und berufliche Entwicklung, das in der heutigen schnelllebigen Technologiebranche unerlässlich ist.

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Viele IT-Leute sind sich einig, dass Amazon DOP-C02 Zertifikat ein Sprungbrett zu dem Höhepunkt der IT-Branche ist. Deshalb kümmern sich viele IT-Experten um die Amazon DOP-C02 Zertifizierungsprüfung.

Die Amazon DOP-C02 Prüfung ist für IT-Profis konzipiert, die ihre Fähigkeiten und Kenntnisse in der Entwicklung und Bereitstellung von Anwendungen auf der Amazon Web Services (AWS) Plattform validieren möchten. Diese Zertifizierung richtet sich an Personen, die Erfahrung mit AWS-Technologien und -Diensten haben und die in DevOps-Praktiken und -Prinzipien versiert sind. Die DOP-C02 Prüfung ist die aktualisierte Version der AWS Certified DevOps Engineer - Professional Zertifizierung, die erstmals 2018 vorgestellt wurde.

Amazon AWS Certified DevOps Engineer - Professional DOP-C02 Prüfungsfragen mit Lösungen (Q297-Q302):

297. Frage
A rapidly growing company wants to scale for developer demand for AWS development environments. Development environments are created manually in the AWS Management Console. The networking team uses AWS CloudFormation to manage the networking infrastructure, exporting stack output values for the Amazon VPC and all subnets. The development environments have common standards, such as Application Load Balancers, Amazon EC2 Auto Scaling groups, security groups, and Amazon DynamoDB tables.
To keep up with demand, the DevOps engineer wants to automate the creation of development environments. Because the infrastructure required to support the application is expected to grow, there must be a way to easily update the deployed infrastructure. CloudFormation will be used to create a template for the development environments.
Which approach will meet these requirements and quickly provide consistent AWS environments for developers?

A. Use Fn::ImportValue intrinsic functions in the Parameters section of the root template to retrieve Virtual Private Cloud (VPC) and subnet values. Define the development resources in the order they need to be created in the CloudFormation nested stacks. Use the CreateChangeSet. and ExecuteChangeSet commands to update existing development environments.
B. Use nested stacks to define common infrastructure components. Use Fn::ImportValue intrinsic functions with the resources of the nested stack to retrieve Virtual Private Cloud (VPC) and subnet values. Use the CreateChangeSet and ExecuteChangeSet commands to update existing development environments.
C. Use nested stacks to define common infrastructure components. To access the exported values, use TemplateURL to reference the networking team's template. To retrieve Virtual Private Cloud (VPC) and subnet values, use Fn::ImportValue intrinsic functions in the Parameters section of the root template. Use the CreateChangeSet and ExecuteChangeSet commands to update existing development environments.
D. Use Fn::ImportValue intrinsic functions in the Resources section of the template to retrieve Virtual Private Cloud (VPC) and subnet values. Use CloudFormation StackSets for the development environments, using the Count input parameter to indicate the number of environments needed. Use the UpdateStackSet command to update existing development environments.

Antwort: B

Begründung:
https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/intrinsic-function-reference-importvalue.html
https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/intrinsic-function-reference-importvalue.html CF of network exports the VPC, subnet or needed information CF of application imports the above information to its stack and UpdateChangeSet/ ExecuteChangeSet

298. Frage
A company uses an Amazon Elastic Kubernetes Service (Amazon EKS) cluster to host its machine learning (ML) application. As the ML model and the container image grow, pod startup time has increased to several minutes. The DevOps engineer created an EventBridge rule that triggers Systems Manager automation to prefetch container images from ECR. Node groups and the cluster have tags configured.
What should the DevOps engineer do next to meet the requirements?

A. Create an IAM role allowing EventBridge to use Systems Manager to run commands in the worker nodes. Create a State Manager association using the nodes' tags to prefetch container images.
B. Create an IAM role allowing EventBridge to use Systems Manager to run commands in the control plane nodes. Create a State Manager association using the nodes' tags.
C. Create an IAM role allowing EventBridge to use Systems Manager to run commands in the control plane nodes. Create a State Manager association using control plane node tags.
D. Create an IAM role allowing EventBridge to use Systems Manager to run commands in the worker nodes. Create a State Manager association using the nodes' machine size.

Antwort: A

Begründung:
Comprehensive & Detailed Explanation (150-250 words):
EKS control plane nodes are fully managed by AWS and are not accessible for configuration, SSH, SSM commands, or custom workloads. Therefore, any prefetching of container images must occur on the worker nodes (EC2 instances in the node groups) because those are the machines that pull and cache container images before running pods.
Option C is the only solution that aligns with AWS architecture:
It creates an IAM role allowing Systems Manager Run Command to execute on EC2 worker nodes.
It uses node group tags to target the correct nodes dynamically.
State Manager runs the prefetch script whenever triggered by the EventBridge automation - ensuring newly added nodes pre-pull images and reduce cold-start latency to seconds.
Options A and D are invalid because you cannot run Systems Manager commands on EKS control plane nodes; AWS manages them and does not expose them. Option B incorrectly targets node groups based on machine size, which does not provide reliable node identification or filtering.
Thus, Option C provides the correct, scalable, and supported method for prefetching images across all worker nodes.

299. Frage
A DevOps team incorporates automatic load and stress testing capabilities into a CI/CD pipeline by using AWS CodeBuild. The DevOps team uses an Amazon Aurora MySQL DB cluster in a testing environment to perform load testing. The DevOps team set the minimum capacity for the testing DB cluster to 1. The DevOps team uses 10 DB clusters in a production environment. The DevOps team has set all the DB clusters to automatically scale up to a maximum capacity of 15 Aurora Replicas.
When the DevOps team performs load testing, the configuration of the Aurora DB cluster in the testing environment prevents the cluster from scaling rapidly enough to meet the transient demand, which causes load testing to fail.
The DevOps team must configure the DB cluster in the testing environment to meet the load-testing demand.
Which solution will meet this requirement MOST cost-effectively?

A. Set the minimum capacity to 10 in the POST_BUILD phase of the CodeBuild configuration. Reset the capacity to 1 in the same POST_BUILD phase.
B. Add an Amazon RDS Proxy in front of the testing Aurora DB cluster. Set the RDS Proxy endpoint in the PRE_BUILD phase of the CodeBuild configuration.
C. Set the minimum capacity to 10 in the PRE_BUILD phase of the CodeBuild configuration. Reset the capacity to 1 in the POST_BUILD phase.
D. Set the minimum capacity to 10. Reduce the scale-in cooldown period in the PRE_BUILD phase of the CodeBuild configuration.

Antwort: C

Begründung:
The failure occurs because the test Aurora cluster starts with too little minimum replica capacity and cannot scale out fast enough for short-lived load testing. Aurora Auto Scaling uses minimum and maximum capacity values for the DB cluster, and scale-out and scale-in behavior is controlled by cooldown behavior. Raising the minimum capacity before the load test pre-warms the cluster with enough Aurora Replicas to absorb the transient load. Resetting the minimum back to 1 in POST_BUILD keeps the solution cost-effective after testing finishes. Option A does not reset capacity and changes scale-in behavior rather than fixing pre-test scale-out lag. Option C changes capacity after the test, which is too late. RDS Proxy manages connections, not Aurora replica scale-out capacity.

300. Frage
A company has a mobile application that makes HTTP API calls to an Application Load Balancer (ALB). The ALB routes requests to an AWS Lambda function. Many different versions of the application are in use at any given time, including versions that are in testing by a subset of users. The version of the application is defined in the user-agent header that is sent with all requests to the API.
After a series of recent changes to the API, the company has observed issues with the application. The company needs to gather a metric for each API operation by response code for each version of the application that is in use. A DevOps engineer has modified the Lambda function to extract the API operation name, version information from the user-agent header and response code.
Which additional set of actions should the DevOps engineer take to gather the required metrics?

A. Configure the ALB access logs to write to an Amazon CloudWatch Logs log group. Modify the Lambda function to respond to the ALB with the API operation name, response code, and version number as response metadata. Configure a CloudWatch Logs metric filter that increments a metric for each API operation name. Specify response code and application version as dimensions for the metric.
B. Modify the Lambda function to write the API operation name, response code, and version number as a log line to an Amazon CloudWatch Logs log group. Configure a CloudWatch Logs Insights query to populate CloudWatch metrics from the log lines. Specify response code and application version as dimensions for the metric.
C. Modify the Lambda function to write the API operation name, response code, and version number as a log line to an Amazon CloudWatch Logs log group. Configure a CloudWatch Logs metric filter that increments a metric for each API operation name. Specify response code and application version as dimensions for the metric.
D. Configure AWS X-Ray integration on the Lambda function. Modify the Lambda function to create an X-Ray subsegment with the API operation name, response code, and version number. Configure X-Ray insights to extract an aggregated metric for each API operation name and to publish the metric to Amazon CloudWatch. Specify response code and application version as dimensions for the metric.

Antwort: C

Begründung:
Explanation
"Note that the metric filter is different from a log insights query, where the experience is interactive and provides immediate search results for the user to investigate. No automatic action can be invoked from an insights query. Metric filters, on the other hand, will generate metric data in the form of a time series. This lets you create alarms that integrate into your ITSM processes, execute AWS Lambda functions, or even create anomaly detection models."
https://aws.amazon.com/blogs/mt/quantify-custom-application-metrics-with-amazon-cloudwatch-logs-and-metri

301. Frage
A DevOps team is merging code revisions for an application that uses an Amazon RDS Multi-AZ DB cluster for its production database. The DevOps team uses continuous integration to periodically verify that the application works. The DevOps team needs to test the changes before the changes are deployed to the production database.
Which solution will meet these requirements'?

A. Create a snapshot of the DB duster before deploying the application Use the Update requires Replacement property on the DB instance in AWS CloudFormation to deploy the application and apply the changes.
B. Ensure that the DB cluster is a Multi-AZ deployment. Deploy the application with the updates. Fail over to the standby instance if verification fails.
C. Deploy the application to production. Configure an audit log of data control language (DCL) operations to capture database activities to perform if verification fails.
D. Use a buildspec file in AWS CodeBuild to restore the DB cluster from a snapshot of the production database run integration tests, and drop the restored database after verification.

Antwort: D

Begründung:
Explanation
This solution will meet the requirements because it will create a temporary copy of the production database using a snapshot, run the integration tests on the copy, and delete the copy after the tests are done. This way, the production database will not be affected by the code revisions, and the DevOps team can test the changes before deploying them to production. A buildspec file is a YAML file that contains the commands and settings that CodeBuild uses to run a build1. The buildspec file can specify the steps to restore the DB cluster from a snapshot, run the integration tests, and drop the restored database2

302. Frage
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