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Amazon AWS Certified DevOps Engineer - Professional Sample Questions (Q273-Q278):

NEW QUESTION # 273
Using the AWS CLI, which command retrieves CloudTrail trail settings, including the status of the trail itself?

A. aws cloudtrail return-trails
B. aws cloudtrail describe-trails
C. aws cloudtrail get-settings
D. aws cloudtrail validate-settings

Answer: B

Explanation:
You can retrieve trail settings and status using the cloudtrail describe-trails command. It will generate output similar to the example below.
{
"trailList": [
{
"IncludeGlobalServiceEvents": false,
"Name": "trailname",
"S3KeyPrefix": "my-prefix",
"TrailARN": "arn:aws:cloudtrail:us-east-2:123456789012:trail/my-trail",
"LogFileValidationEnabled": true,
"IsMultiRegionTrail": true,
"HasCustomEventSelectors": false,
"S3BucketName": " bucket"
"SnsTopicName": " topic",
"HomeRegion": "us-east-2"
}
]
}
Reference:
http://docs.aws.amazon.com/awscloudtrail/latest/userguide/cloudtrail-create-and-update-a-trail- by-using-the-aws-cli.html

NEW QUESTION # 274
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. 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.
B. 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.
C. Create a script that uses the CodeDeploy environment variable DEPLOYMENT_GROUP_NAME to identify which deployment group the instances is part of. Use this information to configure the log level settings. Reference this script as part of the Beforelnstall lifecycle hook in the appspec.yml file
D. 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.

Answer: D

NEW QUESTION # 275
You need to deploy a new application version to production. Because the deployment is high-risk, you need to roll the new version out to users over a number of hours, to make sure everything is working correctly. You need to be able to control the proportion of users seeing the new version of the application down to the percentage point.
You use ELB and EC2 with Auto Scaling Groups and custom AMIs with your code pre-installed assigned to Launch Configurations. There are no database-level changes during your deployment. You have been told you cannot spend too much money, so you must not increase the number of EC2 instances much at all during the deployment, but you also need to be able to switch back to the original version of code quickly if something goes wrong. What is the best way to meet these requirements?

A. Create a second ELB, Auto Scaling Launch Configuration, and Auto Scaling Group using the Launch Configuration. Create AMIs with all code pre-installed. Assign the new AMI to the second Auto Scaling Launch Configuration. Use Route53 Weighted Round Robin Records to adjust the proportion of traffic hitting the two ELBs.
B. Migrate to use AWS Elastic Beanstalk. Use the established and well-tested Rolling Deployment setting AWS provides on the new Application Environment, publishing a zip bundle of the new code and adjusting the wait period to spread the deployment over time. Re-deploy the old code bundle to rollback if needed.
C. Create AMIs with all code pre-installed. Assign the new AMI to the Auto Scaling Launch Configuration, to replace the old one. Gradually terminate instances running the old code (launched with the old Launch Configuration) and allow the new AMIs to boot to adjust the traffic balance to the new code. On rollback, reverse the process by doing the same thing, but changing the AMI on the Launch Config back to the original code.
D. Use the Blue-Green deployment method to enable the fastest possible rollback if needed. Create a full second stack of instances and cut the DNS over to the new stack of instances, and change the DNS back if a rollback is needed.

Answer: A

Explanation:
Only Weighted Round Robin DNS Records and reverse proxies allow such fine-grained tuning of traffic splits. The Blue-Green option does not meet the requirement that we mitigate costs and keep overall EC2 fleet size consistent, so we must select the 2 ELB and ASG option with WRR DNS tuning. This method is called A/B deployment and/or Canary deployment.
https://d0.awsstatic.com/whitepapers/overview-of-deployment-options-on-aws.pdf

NEW QUESTION # 276
You are designing a service that aggregates clickstream data in batch and delivers reports to subscribers via
email only once per week. Data is extremely spikey, geographically distributed, high-scale, and unpredictable.
How should you design this system?

A. Use a CloudFront distribution with access log delivery to S3. Clicks should be recorded as querystring
GETs to the distribution. Reports are built and sent by periodically running EMRjobs over the access
logs in S3.
C Use API Gateway invoking Lambdas which PutRecords into Kinesis, and EMR running Spark
performing GetRecords on Kinesis to scale with spikes. Spark on EMR outputs the analysis to S3, which
are sent out via email.
D- Use AWS Elasticsearch service and EC2 Auto Scaling groups. The Autoscaling groups scale based
on click throughput and stream into the Elasticsearch domain, which is also scalable. Use Kibana to
generate reports periodically.
B. Use a large RedShift cluster to perform the analysis, and a fleet of Lambdas to perform record inserts
into the RedShift tables. Lambda will scale rapidly enough for the traffic spikes.

Answer: A

Explanation:
Explanation
When you look at building reports or analyzing data from a large data set, you need to consider CMR because
this service is built on the Hadoop framework which is used to processes large data sets.
The ideal approach to getting data onto CMR is to use S3. Since the Data is extremely spikey and
geographically distributed, using edge locations via Cloudfront distributions is the best way to fetch the data.
Option A is invalid because RedShift is more of a petabyte storage cluster.
Option C is invalid because having both Kinesis and CMR for the job analysis is redundant.
Option D is invalid because Elastic Search is not an option for processing records.
For more information on Amazon CMR, please visit the below URL:
* https://aws.amazon.com/emr/

NEW QUESTION # 277
A company has microservices running in AWS Lambda that read data from Amazon DynamoDB. The Lambda code is manually deployed by Developers after successful testing. The company now needs the tests and deployments be automated and run in the cloud. Additionally, traffic to the new versions of each microservice should be incrementally shifted over time after deployment.
What solution meets all the requirements, ensuring the MOST developer velocity?

A. Create an AWS CodePipeline configuration and set up the source code step to trigger when code is pushed. Set up the build step to use AWS CodeBuild to run the tests. Set up an AWS CodeDeploy configuration to deploy, then select the CodeDeployDefault.LambdaLinear10PercentEvery3Minutes option.
B. Create an AWS CodePipeline configuration and set up a post-commit hook to trigger the pipeline after tests have passed. Use AWS CodeDeploy and create a Canary deployment configuration that specifies the percentage of traffic and interval.
C. Use the AWS CLI to set up a post-commit hook that uploads the code to an Amazon S3 bucket after tests have passed. Set up an S3 event trigger that runs a Lambda function that deploys the new version. Use an interval in the Lambda function to deploy the code over time at the required percentage.
D. Create an AWS CodeBuild configuration that triggers when the test code is pushed. Use AWS CloudFormation to trigger an AWS CodePipeline configuration that deploys the new Lambda versions and specifies the traffic shift percentage and interval.

Answer: A

NEW QUESTION # 278
......

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