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A company recently migrated its application to an Amazon Elastic Kubernetes Service
(Amazon EKS) cluster that uses Amazon EC2 instances. The company configured the
application to automatically scale based on CPU utilization.
The application produces memory errors when it experiences heavy loads. The application
also does not scale out enough to handle the increased load. The company needs to collect and analyze memory metrics for the application over time.
Which combination of steps will meet these requirements? (Select THREE.)
A. Attach the Cloud WatchAgentServer Pol icy managed 1AM policy to the 1AM instance profile that the cluster uses.
B. Attach the Cloud WatchAgentServer Pol icy managed 1AM policy to a service account role for the cluster.
C. Collect performance metrics by deploying the unified Amazon CloudWatch agent to the existing EC2 instances in the cluster. Add the agent to the AMI for any new EC2 instances that are added to the cluster.
D. Collect performance logs by deploying the AWS Distro for OpenTelemetry collector as a DaemonSet.
E. Analyze the pod_memory_utilization Amazon CloudWatch metric in the Containerlnsights namespace by using the Service dimension.
F. Analyze the node_memory_utilization Amazon CloudWatch metric in the Containerlnsights namespace by using the ClusterName dimension.
A company is developing a new application. The application uses AWS Lambda functions
for its compute tier. The company must use a canary deployment for any changes to the
Lambda functions. Automated rollback must occur if any failures are reported.
The company’s DevOps team needs to create the infrastructure as code (IaC) and the
CI/CD pipeline for this solution.
Which combination of steps will meet these requirements? (Choose three.)
A. Create an AWS CloudFormation template for the application. Define each Lambda function in the template by using the AWS::Lambda::Function resource type. In the template, include a version for the Lambda function by using the AWS::Lambda::Version resource type. Declare the CodeSha256 property. Configure an AWS::Lambda::Alias resource that references the latest version of the Lambda function.
B. Create an AWS Serverless Application Model (AWS SAM) template for the application. Define each Lambda function in the template by using the AWS::Serverless::Function resource type. For each function, include configurations for the AutoPublishAlias property and the DeploymentPreference property. Configure the deployment configuration type to LambdaCanary10Percent10Minutes.
C. Create an AWS CodeCommit repository. Create an AWS CodePipeline pipeline. Use
the CodeCommit repository in a new source stage that starts the pipeline. Create an AWS
CodeBuild project to deploy the AWS Serverless ApplicationModel (AWS SAM) template.
Upload the template and source code to the CodeCommit repository. In the CodeCommit
repository, create a buildspec.yml file that includes the commands to build and deploy the
SAM application.
D. Create an AWS CodeCommit repository. Create an AWS CodePipeline pipeline. Use the CodeCommit repository in a new source stage that starts the pipeline. Create an AWS CodeDeploy deployment group that is configured for canary deployments with a DeploymentPreference type of Canary10Percent10Minutes. Upload the AWS CloudFormation template and source code to the CodeCommit repository. In the CodeCommit repository, create an appspec.yml file that includes the commands to deploy the CloudFormation template.
E. Create an Amazon CloudWatch composite alarm for all the Lambda functions. Configure an evaluation period and dimensions for Lambda. Configure the alarm to enter the ALARM state if any errors are detected or if there is insufficient data.
F. Create an Amazon CloudWatch alarm for each Lambda function. Configure the alarms to enter the ALARM state if any errors are detected. Configure an evaluation period, dimensions for each Lambda function and version, and the namespace as AWS/Lambda on the Errors metric.
Explanation: The requirement is to create the infrastructure as code (IaC) and the CI/CD
pipeline for the Lambda application that uses canary deployment and automated rollback.
To do this, the DevOps team needs to use the following steps:
Create an AWS Serverless Application Model (AWS SAM) template for the
application. AWS SAM is a framework that simplifies the development and
deployment of serverless applications on AWS. AWS SAM allows customers to
define Lambda functions and other resources in a template by using a simplified
syntax. For each Lambda function, the DevOps team can include configurations
for the AutoPublishAlias property and the DeploymentPreference property. The
AutoPublishAlias property specifies the name of the alias that points to the latest
version of the function. The DeploymentPreference property specifies how
CodeDeploy deploys new versions of the function. By configuring the deployment
configuration type to LambdaCanary10Percent10Minutes, the DevOps team can
enable canary deployment with 10% of traffic shifted to the new version every 10
minutes.
Create an AWS CodeCommit repository. Create an AWS CodePipeline pipeline.
Use the CodeCommit repository in a new source stage that starts the pipeline.
Create an AWS CodeBuild project to deploy the AWS SAM template. CodeCommit
is a fully managed source control service that hosts Git repositories. CodePipeline
is a fully managed continuous delivery service that automates the release process
of software applications. CodeBuild is a fully managed continuous integration
service that compiles source code and runs tests. By using these services, the
DevOps team can create a CI/CD pipeline for the Lambda application. The pipeline should use the CodeCommit repository as the source stage, where the
DevOps team can upload the SAM template and source code. The pipeline should
also use a CodeBuild project as the build stage, where the SAM template can be
built and deployed.
Create an Amazon CloudWatch alarm for each Lambda function. Configure the
alarms to enter the ALARM state if any errors are detected. Configure an
evaluation period, dimensions for each Lambda function and version, and the
namespace as AWS/Lambda on the Errors metric. CloudWatch is a service that
monitors and collects metrics from AWS resources and applications. CloudWatch
alarms are actions that are triggered when a metric crosses a specified threshold.
By creating CloudWatch alarms for each Lambda function, the DevOps team can
monitor the health and performance of each function version during deployment.
By configuring the alarms to enter the ALARM state if any errors are detected, the
DevOps team can enable automated rollback if any failures are reported.
A DevOps engineer manages a large commercial website that runs on Amazon EC2. The
website uses Amazon Kinesis Data Streams to collect and process web togs. The DevOps
engineer manages the Kinesis consumer application, which also runs on Amazon EC2.
Sudden increases of data cause the Kinesis consumer application to (all behind and the
Kinesis data streams drop records before the records can be processed. The DevOps
engineer must implement a solution to improve stream handling.
Which solution meets these requirements with the MOST operational efficiency?
A. Modify the Kinesis consumer application to store the logs durably in Amazon S3 Use Amazon EMR to process the data directly on Amazon S3 to derive customer insights Store the results in Amazon S3.
B. Horizontally scale the Kinesis consumer application by adding more EC2 instances based on the Amazon CloudWatch GetRecords IteratorAgeMilliseconds metric Increase the retention period of the Kinesis data streams.
C. Convert the Kinesis consumer application to run as an AWS Lambda function. Configure the Kinesis data streams as the event source for the Lambda function to process the data streams
D. Increase the number of shards in the Kinesis data streams to increase the overall throughput so that the consumer application processes the data faster.
A company sells products through an ecommerce web application The company wants a
dashboard that shows a pie chart of product transaction details. The company wants to
integrate the dashboard With the company’s existing Amazon CloudWatch dashboards
Which solution Will meet these requirements With the MOST operational efficiency?
A. Update the ecommerce application to emit a JSON object to a CloudWatch log group for each processed transaction. Use CloudWatch Logs Insights to query the log group and to visualize the results in a pie chart format Attach the results to the desired CloudWatch dashboard.
B. Update the ecommerce application to emit a JSON object to an Amazon S3 bucket for each processed transaction. Use Amazon Athena to query the S3 bucket and to visualize the results In a Pie chart format. Export the results from Athena Attach the results to the desired CloudWatch dashboard
C. Update the ecommerce application to use AWS X-Ray for instrumentation. Create a new X-Ray subsegment Add an annotation for each processed transaction. Use X-Ray traces to query the data and to visualize the results in a pie chart format Attach the results to the desired CloudWatch dashboard
D. Update the ecommerce application to emit a JSON object to a CloudWatch log group for each processed transaction_ Create an AWS Lambda function to aggregate and write the results to Amazon DynamoDB. Create a Lambda subscription filter for the log file. Attach the results to the desired CloudWatch dashboard.
A DevOps team manages an API running on-premises that serves as a backend for an
Amazon API Gateway endpoint. Customers have been complaining about high response
latencies, which the development team has verified using the API Gateway latency metrics
in Amazon CloudWatch. To identify the cause, the team needs to collect relevant data
without introducing additional latency.
Which actions should be taken to accomplish this? (Choose two.)
A. Install the CloudWatch agent server side and configure the agent to upload relevant logs to CloudWatch.
B. Enable AWS X-Ray tracing in API Gateway, modify the application to capture request segments, and upload those segments to X-Ray during each request.
C. Enable AWS X-Ray tracing in API Gateway, modify the application to capture request segments, and use the X-Ray daemon to upload segments to X-Ray.
D. Modify the on-premises application to send log information back to API Gateway with each request.
E. Modify the on-premises application to calculate and upload statistical data relevant to the API service requests to CloudWatch metrics.
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