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Amazon AWS Certified DevOps Engineer - Professional Sample Questions (Q272-Q277):

NEW QUESTION # 272
A company has a mission-critical application on AWS that uses automatic scaling The company wants the deployment lilecycle to meet the following parameters.
* The application must be deployed one instance at a time to ensure the remaining fleet continues to serve traffic
* The application is CPU intensive and must be closely monitored
* The deployment must automatically roll back if the CPU utilization of the deployment instance exceeds 85%.
Which solution will meet these requirements?

A. Use AWS Elastic Beanstalk for load balancing and AWS Auto Scaling Configure an alarm tied to the CPU utilization metric Configure rolling deployments with a fixed batch size of one instance Enable enhanced health to monitor the status of the deployment and roll back based on the alarm previously created.
B. Use AWS CodeDeploy with Amazon EC2 Auto Scaling. Configure an alarm tied to the CPU utilization metric. Use the CodeDeployDefault OneAtAtime configuration as a deployment strategy Configure automatic rollbacks within the deployment group to roll back the deployment if the alarm thresholds are breached
C. Use AWS Systems Manager to perform a blue/green deployment with Amazon EC2 Auto Scaling Configure an alarm tied to the CPU utilization metric Deploy updates one at a time Configure automatic rollbacks within the Auto Scaling group to roll back the deployment if the alarm thresholds are breached
D. Use AWS CloudFormalion to create an AWS Step Functions state machine and Auto Scaling hfecycle hooks to move to one instance at a time into a wait state Use AWS Systems Manager automation to deploy the update to each instance and move it back into the Auto Scaling group using the heartbeat timeout

Answer: B

Explanation:
https://aws.amazon.com/about-aws/whats-new/2016/09/aws-codedeploy-introduces-deployment-monitoring-with-amazon-cloudwatch-alarms-and-automatic-deployment-rollback/

NEW QUESTION # 273
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. Increase the number of shards in the Kinesis data streams to increase the overall throughput so that the consumer application processes the data faster.
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. 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.
D. 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

Answer: B

NEW QUESTION # 274
A company is developing an application that will generate log events. The log events consist of five distinct metrics every one tenth of a second and produce a large amount of data The company needs to configure the application to write the logs to Amazon Time stream The company will configure a daily query against the Timestream table.
Which combination of steps will meet these requirements with the FASTEST query performance? (Select THREE.)

A. Configure the memory store retention period to be shorter than the magnetic store retention period
B. Treat each log as a single-measure record
C. Configure the memory store retention period to be longer than the magnetic store retention period
D. Use batch writes to write multiple log events in a Single write operation
E. Treat each log as a multi-measure record
F. Write each log event as a single write operation

Answer: A,D,E

Explanation:
A comprehensive and detailed explanation is:
* Option A is correct because using batch writes to write multiple log events in a single write operation is a recommended practice for optimizing the performance and cost of data ingestion in Timestream.
Batch writes can reduce the number of network round trips and API calls, and can also take advantage of parallel processing by Timestream.Batch writes can also improve the compression ratio of data in the memory store and the magnetic store, which can reduce the storage costs and improve the query performance1.
* Option B is incorrect because writing each log event as a single write operation is not a recommended practice for optimizing the performance and cost of data ingestion in Timestream. Writing each log event as a single write operation would increase the number of network round trips and API calls, and would also reduce the compression ratio of data in the memory store and the magneticstore.This would increase the storage costs and degrade the query performance1.
* Option C is incorrect because treating each log as a single-measure record is not a recommended practice for optimizing the query performance in Timestream. Treating each log as a single-measure record would result in creating multiple records for each timestamp, which would increase the storage size and the query latency.Moreover, treating each log as a single-measure record would require using joins to query multiple measures for the same timestamp, whichwould add complexity and overhead to the query processing2.
* Option D is correct because treating each log as a multi-measure record is a recommended practice for optimizing the query performance in Timestream. Treating each log as a multi-measure record would result in creating a single record for each timestamp, which would reduce the storage size and the query latency.Moreover, treating each log as a multi-measure record would allow querying multiple measures for the same timestamp without using joins, which would simplify and speed up the query processing2.
* Option E is incorrect because configuring the memory store retention period to be longer than the magnetic store retention period is not a valid option in Timestream. The memory store retention period must always be shorter than or equal to the magnetic store retention period.This ensures that data is moved from the memory store to the magnetic store before it expires out of the memory store3.
* Option F is correct because configuring the memory store retention period to be shorter than the magnetic store retention period is a valid option in Timestream. The memory store retention period determines how long data is kept in the memory store, which is optimized for fast point-in-time queries.
The magnetic store retention period determines how long data is kept in the magnetic store, which is optimized for fast analytical queries.By configuring these retentionperiods appropriately, you can balance your storage costs and query performance according to yourapplication needs3.
References:
* 1:Batch writes
* 2:Multi-measure records vs. single-measure records
* 3:Storage

NEW QUESTION # 275
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 companfs existing Amazon CloudWatch dashboards Which solution Will meet these requirements With the MOST operational effictency?

A. 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
B. 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
C. 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.
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.

Answer: C

Explanation:
The correct answer is A.
A comprehensive and detailed explanation is:
Option A is correct because it meets the requirements with the most operational efficiency. Updating the ecommerce application to emit a JSON object to a CloudWatch log group for each processed transaction is a simple and cost-effective way to collect the data needed for the dashboard. Using CloudWatch Logs Insights to query the log group and to visualize the results in a pie chart format is also a convenient and integrated solution that leverages the existing CloudWatch dashboards. Attaching the results to the desired CloudWatch dashboard is straightforward and does not require any additional steps or services.
Option B is incorrect because it introduces unnecessary complexity and cost. Updating the ecommerce application to emit a JSON object to an Amazon S3 bucket for each processed transaction is a valid way to store the data, but it requires creating and managing an S3 bucket and its permissions. Using Amazon Athena to query the S3 bucket and to visualize the results in a pie chart format is also a valid way to analyze the data, but it incurs charges based on the amount of data scanned by each query. Exporting the results from Athena and attaching them to the desired CloudWatch dashboard is also an extra step that adds more overhead and latency.
Option C is incorrect because it uses AWS X-Ray for an inappropriate purpose. Updating the ecommerce application to use AWS X-Ray for instrumentation is a good practice for monitoring and tracing distributed applications, but it is not designed for aggregating product transaction details. Creating a new X-Ray subsegment and adding an annotation for each processed transaction is possible, but it would clutter the X-Ray service map and make it harder to debug performance issues. Using X-Ray traces to query the data and to visualize the results in a pie chart format is also possible, but it would require custom code and logic that are not supported by X-Ray natively. Attaching the results to the desired CloudWatch dashboard is also not supported by X-Ray directly, and would require additional steps or services.
Option D is incorrect because it introduces unnecessary complexity and cost. Updating the ecommerce application to emit a JSON object to a CloudWatch log group for each processed transaction is a simple and cost-effective way to collect the data needed for the dashboard, as in option A) However, creating an AWS Lambda function to aggregate and write the results to Amazon DynamoDB is redundant, as CloudWatch Logs Insights can already perform aggregation queries on log data. Creating a Lambda subscription filter for the log file is also redundant, as CloudWatch Logs Insights can already access log data directly. Attaching the results to the desired CloudWatch dashboard would also require additional steps or services, as DynamoDB does not support native integration with CloudWatch dashboards.
Reference:
CloudWatch Logs Insights
Amazon Athena
AWS X-Ray
AWS Lambda
Amazon DynamoDB

NEW QUESTION # 276
A company needs to ensure that flow logs remain configured for all existing and new VPCs in its AWS account. The company uses an AWS CloudFormation stack to manage its VPCs. The company needs a solution that will work for any VPCs that any IAM user creates.
Which solution will meet these requirements?

A. Create an IAM policy to deny the use of API calls for VPC flow logs. Attach the IAM policy to all IAM users.
B. Create an organization in AWS Organizations. Add the company's AWS account to the organization.
Create an SCP to prevent users from modifying VPC flow logs.
C. Add the resource to the CloudFormation stack that creates the VPCs.
D. Turn on AWS Config. Create an AWS Config rule to check whether VPC flow logs are turned on.
Configure automatic remediation to turn on VPC flow logs.

Answer: D

Explanation:
To meet the requirements of ensuring that flow logs remain configured for all existing and new VPCs in the AWS account, the company should use AWS Config and automatic remediation. AWS Config is a service that enables customers to assess, audit, and evaluate the configurations of their AWS resources. AWS Config continuously monitors and records the configuration changes of the AWS resources and evaluates them against desired configurations. Customers can use AWS Config rules to define the desired configuration state of their AWS resources and trigger actions when a resource configuration violates a rule.
One of the AWS Config rules that customers can use is vpc-flow-logs-enabled, which checks whether VPC flow logs are enabled for all VPCs in an AWS account. Customers can also configure automatic remediation for this rule, which means that AWS Config will automatically enable VPC flow logs for any VPCs that do not have them enabled. Customers can specify the destination (CloudWatch Logs or S3) and the traffic type (all, accept, or reject) for the flow logs as remediation parameters. By using AWS Config and automatic remediation, the company can ensure that flow logs remain configured for all existing and new VPCs in its AWS account, regardless of who creates them or how they are created.
The other options are not correct because they do not meet the requirements or follow best practices. Adding the resource to the CloudFormation stack that creates the VPCs is not a sufficient solution because it will only work for VPCs that are created by using the CloudFormation stack. It will not work for VPCs that are created by using other methods, such as the console or the API. Creating an organization in AWS Organizations and creating an SCP to prevent users from modifying VPC flow logs is not a good solution because it will not ensure that flow logs are enabled for all VPCs in the first place. It will only prevent users from disabling or changing flow logs after they are enabled. Creating an IAM policy to deny the use of API calls for VPC flow logs and attaching it to all IAM users is not a valid solution because it will prevent users from enabling or disabling flow logs at all. It will also not work for VPCs that are created by using other methods, such as the console or CloudFormation.
References:
* 1: AWS::EC2::FlowLog - AWS CloudFormation
* 2: Amazon VPC Flow Logs extends CloudFormation Support to custom format subscriptions, 1-minute aggregation intervals and tagging
* 3: Logging IP traffic using VPC Flow Logs - Amazon Virtual Private Cloud
* : About AWS Config - AWS Config
* : vpc-flow-logs-enabled - AWS Config
* : Remediate Noncompliant Resources with AWS Config Rules - AWS Config

NEW QUESTION # 277
......

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