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Amazon AWS Certified DevOps Engineer - Professional Sample Questions (Q265-Q270):

NEW QUESTION # 265
You have an application running a specific process that is critical to the application's functionality, and have added the health check process to your Auto Scaling Group. The instances are showing healthy but the application itself is not working as it should. What could be the issue with the health check, since it is still showing the instances as healthy.

A. The health check is not configured properly
B. You do not have the time range in the health check properly configured
C. It is not possible for a health check to monitor a process that involves the application
D. The health check is not checking the application process

Answer: D

Explanation:
Explanation
If you have custom health checks, you can send the information from your health checks to Auto Scaling so that Auto Scaling can use this information. For example, if you determine that an instance is not functioning as expected, you can set the health status of the instance to Unhealthy. The next time that Auto Scaling performs a health check on the instance, it will determine that the instance is unhealthy and then launch a replacement instance For more information on Autoscaling health checks, please refer to the below document link: from AWS
* http://docs.aws.amazon.com/autoscaling/latest/userguide/healthcheck.html

NEW QUESTION # 266
Management has reported an increase in the monthly bill from Amazon web services, and they are extremely concerned with this increased cost.
Management has asked you to determine the exact cause of this increase.
After reviewing the billing report, you notice an increase in the data transfer cost.
How can you provide management with a better insight into data transfer use?

A. Use Amazon CloudWatch Logs to run a map-reduce on your logs to determine high usage and data transfer.
B. Update your Amazon CloudWatch metrics to use five-second granularity, which will give better detailed metrics that can be combined with your billing data to pinpoint anomalies.
C. Using Amazon CloudWatch metrics, pull your Elastic Load Balancing outbound data transfer metrics monthly, and include them with your billing report to show which application is causing higher bandwidth usage.
D. Deliver custom metrics to Amazon CloudWatch per application that breaks down application data transfer into multiple, more specific data points.

Answer: D

NEW QUESTION # 267
An application is being deployed with two Amazon EC2 Auto Scaling groups, each configured with an Application Load Balancer. The application is deployed to one of the Auto Scaling groups and an Amazon Route 53 alias record is pointed to the Application Load Balancer of the last deployed Auto Scaling group. Deployments alternate between the two Auto Scaling groups.
Home security devices are making requests into the application. The Development team notes that new requests are coming into the old stack days after the deployment. The issue is caused by devices that are not observing the Time to Live (TTL) setting on the Amazon Route 53 alias record. What steps should the DevOps Engineer take to address the issue with requests coming to the old stacks, while creating minimal additional resources?

A. Move the application to an AWS Elastic Beanstalk application with two environments. Perform new deployments on the non-live environment. After a deployment, perform an Elastic Beanstalk CNAME swap to make the newly deployed environment the live environment.
B. Create an Amazon CloudFront distribution. Set the two existing Application Load Balancers as origins on the distribution. After a deployment, update the CloudFront distribution behavior to send requests to the newly deployed Auto Scaling group.
C. Create a fleet of Amazon EC2 instances running HAProxy behind an Application Load Balancer.
The HAProxy instances will proxy the requests to one of the existing Auto Scaling groups. After a deployment the HAProxy instances are updated to send requests to the newly deployed Auto Scaling group.
D. Reduce the application to one Application Load Balancer. Create two target groups named Blue and Green. Create a rule on the Application Load Balancer pointed to a single target group. Add logic to the deployment to update the Application Load Balancer rule to the target group of the newly deployed Auto Scaling group.

Answer: D

NEW QUESTION # 268
You are building out a layer in a software stack on AWS that needs to be able to scale out to react to increased
demand as fast as possible. You are running the code on EC2 instances in an Auto Scaling Group behind an
ELB. Which application code deployment method should you use?

A. Create a Dockerfile when preparing to deploy a new version to production and publish it to S3. Use
UserData in the Auto Scaling Launch configuration to pull down the Dockerfile from S3 and run it when
new instances launch.
B. SSH into new instances that come online, and deploy new code onto the system by pulling it from an S3
bucket, which is populated by code that you refresh from source control on new pushes.
C. Bake an AMI when deploying new versions of code, and use that AMI for the Auto Scaling Launch
Configuration.
D. Create a new Auto Scaling Launch Configuration with UserData scripts configured to pull the latest
code at all times.

Answer: C

Explanation:
Explanation
Since the time required to spin up an instance is required to be fast, its better to create an AMI rather than use
User Data. When you use User Data, the script will be
run during boot up, and hence this will be slower.
An Amazon Machine Image (AMI) provides the information required to launch an instance, which is a virtual
server in the cloud. You specify an AM I when you launch
an instance, and you can launch as many instances from the AMI as you need. You can also launch instances
from as many different AMIs as you need.
For more information on the AMI, please refer to the below link:
* http://docs.aws.amazon.com/AWSCC2/latest/UserGuide/AMIs.html

NEW QUESTION # 269
Your Company wants to perform A/B testing on a new website feature for 20 percent of its users.
The website uses CloudFront for whole site delivery, with some content cached for up to 24 hours.
How do you enable this testing for the required proportion of users while minimizing performance impact?

A. Configure the CloudFront distribution to forward a cookie specific to this feature.
For requests where the cookie is not set, the web servers set its value to ''on" for 20 percent of responses and "off" for 80 percent.
For requests where the cookie is set, the web servers use Its value to determine whether the feature should be on or off for the response.
B. Configure the web servers to handle two domain names.
The feature is switched on or off depending on which domain name is used for a request.
Configure a CloudFront origin for each domain name, and configure the CloudFront distribution to use one origin for 20 percent of users and the other origin for the other 80 percent.
C. Invalidate all of the CloudFront distribution's cache items that the feature affects.
On future requests, the web servers create responses with the feature on for 20 percent of users, and off for 80 percent.
The web servers set "Cache-Control: no-cache" on all of these responses.
D. Create a second stack of web servers that host the website with the feature on.
Using Amazon Route53, create two resource record sets with the same name: one with a weighting of "1" and a value of this new stack; the other a weighting of "4" and a value of the existing stack. Use the resource record set's name as the CloudFront distribution's origin.

Answer: A

NEW QUESTION # 270
......

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