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参考のためにいくつかの利点を提供しています。一方では、DOP-C02学習の質問により、作業スタッフが顧客の多様で進化する期待を理解し、その理解を戦略に取り入れることで、DOP-C02試験エンジンを100％信頼できます。一方、プロのDOP-C02学習資料が高い合格率を決定します。調査統計によると、当社製品を使用した後の99％の候補者がDOP-C02試験に合格したことを自信を持って伝えることができます。
Amazon DOP -C02（AWS認定DevOps Engineer -Professional）認定試験は、さまざまなDevOpsプラクティスとAWSプラットフォームでそれらを実装する方法を深く理解している個人向けに設計されています。この認定は、AWS上の高度に利用可能、スケーラブル、およびフォールトトレラントシステムを設計、展開、操作、および管理する個人の能力を検証します。
AmazonのDOP-C02認証は業界で高く評価され、世界中の企業に認められています。この認証は、AWSプラットフォーム上で高可用性、耐障害性、スケーラブルなシステムの設計、展開、および管理の専門知識を持つ候補者を示し、多くのキャリアチャンスを開くことができます。
Amazon知識ベースの経済の支配下で、私たちは変化する世界に歩調を合わせ、まともな仕事とより高い生活水準を追求して知識を更新しなければなりません。 この状況では、ポケットにDOP-C02認定を取得すると、Topexam労働市場での競争上の優位性を完全に高め、他の求職者との差別化を図ることができます。 したがって、当社のDOP-C02学習ガイドは、夢を実現するための献身的な支援を提供します。 そして、DOP-C02試験の質問で20〜30時間学習AWS Certified DevOps Engineer - Professionalした後にのみ、DOP-C02試験に合格することができます。
質問 # 139
A company has an application that runs on AWS Lambda and sends logs to Amazon CloudWatch Logs. An Amazon Kinesis data stream is subscribed to the log groups in CloudWatch Logs. A single consumer Lambda function processes the logs from the data stream and stores the logs in an Amazon S3 bucket.
The company's DevOps team has noticed high latency during the processing and ingestion of some logs.
Which combination of steps will reduce the latency? (Select THREE.)
正解：A、C、F
解説：
Explanation
The latency in processing and ingesting logs can be caused by several factors, such as the throughput of the Kinesis data stream, the concurrency of the Lambda function, and the configuration of the event source mapping. To reduce the latency, the following steps can be taken:
* Create a data stream consumer with enhanced fan-out. Set the Lambda function that processes the logs as the consumer. This will allow the Lambda function to receive records from the data stream with dedicated throughput of up to 2 MB per second per shard, independent of other consumers1. This will reduce the contention and delay in accessing the data stream.
* Increase the ParallelizationFactor setting in the Lambda event source mapping. This will allow the Lambda service to invoke more instances of the function concurrently to process the records from the data stream2. This will increase the processing capacity and reduce the backlog of records in the data stream.
* Configure reserved concurrency for the Lambda function that processes the logs. This will ensure that the function has enough concurrency available to handle the increased load from the data stream3. This will prevent the function from being throttled by the account-level concurrency limit.
The other options are not effective or may have negative impacts on the latency. Option D is not suitable because increasing the batch size in the Kinesis data stream will increase the amount of data that the Lambda function has to process in each invocation, which may increase the execution time and latency4. Option E is not advisable because turning off the ReportBatchItemFailures setting in the Lambda event source mapping will prevent the Lambda service from retrying the failed records, which may result in data loss. Option F is not necessary because increasing the number of shards in the Kinesis data stream will increase the throughput of the data stream, but it will not affect the processing speed of the Lambda function, which is the bottleneck in this scenario.
References:
* 1: Using AWS Lambda with Amazon Kinesis Data Streams - AWS Lambda
* 2: AWS Lambda event source mappings - AWS Lambda
* 3: Managing concurrency for a Lambda function - AWS Lambda
* 4: AWS Lambda function scaling - AWS Lambda
* : AWS Lambda event source mappings - AWS Lambda
* : Scaling Amazon Kinesis Data Streams with AWS CloudFormation - Amazon Kinesis Data Streams
質問 # 140
A company deploys a web application on Amazon EC2 instances that are behind an Application Load Balancer (ALB). The company stores the application code in an AWS CodeCommit repository. When code is merged to the main branch, an AWS Lambda function invokes an AWS CodeBuild project. The CodeBuild project packages the code, stores the packaged code in AWS CodeArtifact, and invokes AWS Systems Manager Run Command to deploy the packaged code to the EC2 instances.
Previous deployments have resulted in defects, EC2 instances that are not running the latest version of the packaged code, and inconsistencies between instances.
Which combination of actions should a DevOps engineer take to implement a more reliable deployment solution? (Select TWO.)
正解：D、E
解説：
To implement a more reliable deployment solution, a DevOps engineer should take the following actions:
* Create a pipeline in AWS CodePipeline that uses the CodeCommit repository as a source provider.
Configure pipeline stages that run the CodeBuild project in parallel to build and test the application. In the pipeline, pass the CodeBuild project output artifact to an AWS CodeDeploy action. This action will improve the deployment reliability by automating the entire process from code commit to deployment, reducing human errors and inconsistencies. By running the build and test stages in parallel, the pipeline can also speed up the delivery time and provide faster feedback. By using CodeDeploy as the deployment action, the pipeline can leverage the features of CodeDeploy, such as traffic shifting, health checks, rollback, and deployment configuration123
* Create an AWS CodeDeploy application and a deployment group to deploy the packaged code to the EC2 instances. Configure the ALB for the deployment group. This action will improve the deployment reliability by using CodeDeploy to orchestrate the deployment across multiple EC2 instances behind an ALB. CodeDeploy can perform blue/green deployments or in-place deployments with traffic shifting, which can minimize downtime and reduce risks. CodeDeploy can also monitor the health of the instances during and after the deployment, and automatically roll back if any issues are detected. By configuring the ALB for the deployment group, CodeDeploy can register and deregister instances from the load balancer as needed, ensuring that only healthy instances receive traffic45 The other options are not correct because they do not improve the deployment reliability or follow best practices. Creating separate pipeline stages that run aCodeBuild project to build and then test the application is not a good option because it will increase the pipeline execution time and delay the feedback loop. Creating individual Lambda functions that use CodeDeploy instead of Systems Manager to run build, test, and deploy actions is not a valid option because it will add unnecessary complexity and cost to the solution. Lambda functions are not designed for long-running tasks such as building or deploying applications. Creating an Amazon S3 bucket and modifying the CodeBuild project to store the packages in the S3 bucket instead of in CodeArtifact is not a necessary option because it will not affect the deployment reliability. CodeArtifact is a secure, scalable, and cost-effective package management service that can store and share software packages for application development67 References:
* 1: What is AWS CodePipeline? - AWS CodePipeline
* 2: Create a pipeline in AWS CodePipeline - AWS CodePipeline
* 3: Deploy an application with AWS CodeDeploy - AWS CodePipeline
* 4: What is AWS CodeDeploy? - AWS CodeDeploy
* 5: Configure an Application Load Balancer for your blue/green deployments - AWS CodeDeploy
* 6: What is AWS Lambda? - AWS Lambda
* 7: What is AWS CodeArtifact? - AWS CodeArtifact
質問 # 141
A DevOps team is deploying microservices for an application on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster. The cluster uses managed node groups.
The DevOps team wants to enable auto scaling for the microservice Pods based on a specific CPU utilization percentage. The DevOps team has already installed the Kubernetes Metrics Server on the cluster.
Which solution will meet these requirements in the MOST operationally efficient way?
正解：A
解説：
Comprehensive and Detailed Explanation From Exact Extract:
To scale microservice Pods based on CPU utilization, the Kubernetes Horizontal Pod Autoscaler (HPA) uses the Kubernetes Metrics Server to monitor resource usage and automatically adjusts the number of Pods.
However, scaling Pods may require additional nodes if the current node capacity is insufficient.
* TheCluster Autoscalerworks with EKS managed node groups to add or remove worker nodes based on pending Pod requirements and resource usage.
* By deploying both HPA and Cluster Autoscaler, the system can automatically scale Pods and add nodes as necessary, ensuring efficient resource utilization and availability.
* Configuring the Cluster Autoscaler with auto-discovery allows it to manage node groups without manual intervention, reducing operational effort.
* Option A only scales nodes based on node CPU utilization, not Pods.
* Option B uses VPA recommender mode, which only suggests resource changes and does not scale automatically.
* Option C involves manual updates and is not automated scaling.Therefore, option D provides the most operationally efficient, fully automated scaling solution.
Reference from AWS Official Documentation:
* Kubernetes Horizontal Pod Autoscaler:"HPA automatically scales the number of Pods based on observed CPU utilization or other metrics."(Kubernetes HPA)
* Cluster Autoscaler on Amazon EKS:"The Cluster Autoscaler automatically adjusts the size of the Kubernetes cluster when there are Pods that fail to run due to insufficient resources or when nodes in the cluster are underutilized."(AWS EKS Cluster Autoscaler)
質問 # 142
A company is launching an application that stores raw data in an Amazon S3 bucket. Three applications need to access the data to generate reports. The data must be redacted differently for each application before the applications can access the data.
Which solution will meet these requirements?
正解：A
解説：
Explanation
The best solution is to use S3 Object Lambda1, which allows you to add your own code to S3 GET, LIST, and HEAD requests to modify and process data as it is returned to an application2. This way, you can redact the data differently for each application without creating and storing multiple copies of the data or running proxies.
The other solutions are less efficient or scalable because they require replicating the data to multiple buckets, streaming the data through Kinesis, or storing the data in S3 access points.
References: 1: Amazon S3 Features | Object Lambda | AWS 2: Transforming objects with S3 Object Lambda - Amazon Simple Storage Service
質問 # 143
A company deploys its corporate infrastructure on AWS across multiple AWS Regions and Availability Zones. The infrastructure is deployed on Amazon EC2 instances and connects with AWS loT Greengrass devices. The company deploys additional resources on on-premises servers that are located in the corporate headquarters.
The company wants to reduce the overhead involved in maintaining and updating its resources. The company's DevOps team plans to use AWS Systems Manager to implement automated management and application of patches. The DevOps team confirms that Systems Manager is available in the Regions that the resources are deployed m Systems Manager also is available in a Region near the corporate headquarters.
Which combination of steps must the DevOps team take to implement automated patch and configuration management across the company's EC2 instances loT devices and on-premises infrastructure? (Select THREE.)
正解：A、D、F
解説：
Explanation
https://aws.amazon.com/blogs/mt/how-to-centrally-manage-aws-iot-greengrass-devices-using-aws-systems-mana
質問 # 144
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