Question 1

A company is developing a web application that runs on Amazon EC2 Linux instances. The application requires monitoring of custom performance metrics. The company must collect metrics for API response times and database query latency across multiple instances. Which solution will generate the custom metrics with the LEAST operational overhead?

Options :

A : Install the Amazon CloudWatch agent on the instances. Configure the agent to collect the custom metrics. Instrument the application to send the metrics to the agent.

B : Use Amazon Managed Service for Prometheus to scrape the custom metrics from the application. Use the Amazon CloudWatch agent to forward the metrics to CloudWatch.

C : Create a custom AWS Lambda function that polls the application endpoints and database at regular intervals. Program the Lambda function to calculate the custom metrics and to send the metrics to Amazon CloudWatch by using PutMetricData API calls.

D : Implement custom logging in the application code to record the custom metrics. Use Amazon CloudWatch Logs Insights to extract and analyze the metrics.

Answer: A

Question 2

An online data analytics application is launched to 12 On-Demand EC2 instances across three Availability Zones using a golden AMI in AWS. Each instance has only 10% utilization after business hours but increases to 30% utilization during peak hours. There are also some third-party applications that use the application from all over the globe with no specific schedule. In the morning, there is always a sudden CPU utilization increase on the EC2 instances due to the number of users logging in to use the application. However, its CPU utilization usually stabilizes after a few hours. A DevOps Engineer has been instructed to reduce costs and improve the overall reliability of the system.

Which among the following options provides the MOST suitable solution in this scenario?

Options :

A : Launch a group of Scheduled Reserved Instances that regularly run before and after the peak hours. Integrate CloudWatch Events and AWS Lambda to regularly stop nine instances after the peak hours every day and restart the nine instances before the business day begins.

B : Set up two Amazon Eventbridge rules and two Lambda functions. Configure each Amazon Eventbridge rule to invoke a Lambda function and regularly run before and after the peak hours. The first function should stop nine instances after the peak hours end while the second function should restart the nine instances before the business day begins

C : Set up an Auto Scaling group using the golden AMI with a scaling action based on the CPU Utilization average. Configure a scheduled action for the group to adjust the minimum number of Amazon EC2 instances to three after business hours end, and reset to six before business hours begin.

D : Set up two AWS Config rules and two Lambda functions. Configure each rule to invoke a Lambda function and regularly run before and after the peak hours. The first function should stop nine instances after the peak hours end while the second function should restart the nine instances before the business day begins.

Answer: C

Question 3

A telecommunications company has a web portal that requires a cross-region failover. The portal stores its data in an Amazon Aurora database in the primary region (us-west-1) and the Parallel Query feature is also enabled in the database to optimize some of the I/O and computation involved in processing data-intensive queries. The portal uses Route 53 to direct customer traffic to the active region.

Which among the options below should be taken to MINIMIZE downtime of the portal in the event that the primary database fails?

Options :

A : Create a CloudWatch Events rule to periodically invoke a Lambda function that checks the health of the primary Amazon Aurora database every hour. Configure the Lambda function to promote the read replica as the primary if a failure was detected. Update the Route 53 record to redirect traffic from the primary to the secondary region.

B : Configure the Route 53 record to balance traffic between both regions equally using the Failover routing policy. Enable the Aurora multi-master option and set up a Route 53 health check to analyze the health of the databases. Set the Route 53 record to automatically direct all traffic to the secondary region when a primary database fails.

C : Launch a read replica of the primary database to the second region. Set up Amazon RDS Event Notification to publish status updates to an SNS topic. Create a Lambda function subscribed to the topic to monitor database health. Configure the Lambda function to promote the read replica as the primary in the event of a failure. Update the Route 53 record to redirect traffic from the primary region to the secondary region.

D : Set up CloudWatch to monitor the status of the Amazon Aurora database. Create a CloudWatch Events rule to send a Slack message to the SysOps Team using Amazon SNS in the event of a database outage. Instruct the SysOps team to redirect traffic to an S3 static website that displays a downtime message. Manually promote the read replica as the primary instance and verify the portal-s status. Redirect traffic from the S3 website to the secondary region.

Answer: C

Question 4

A company has an application that runs on Amazon EC2 instances that are in an Auto Scaling group. When the application starts up. the application needs to process data from an Amazon S3 bucket before the application can start to serve requests.

The size of the data that is stored in the S3 bucket is growing. When the Auto Scaling group adds new instances, the application now takes several minutes to download and process the data before the application can serve requests. The company must reduce the time that elapses before new EC2 instances are ready to serve requests.

Which solution is the MOST cost-effective way to reduce the application startup time?

Options :

A : Configure a warm pool for the Auto Scaling group with warmed EC2 instances in the Stopped state. Configure an autoscaling:EC2_INSTANCE_LAUNCHING lifecycle hook on the Auto Scaling group. Modify the application to complete the lifecycle hook when the application is ready to serve requests.

B : Increase the maximum instance count of the Auto Scaling group. Configure an autoscaling:EC2_INSTANCE_LAUNCHING lifecycle hook on the Auto Scaling group. Modify the application to complete the lifecycle hook when the application is ready to serve requests.

C : Configure a warm pool for the Auto Scaling group with warmed EC2 instances in the Running state. Configure an autoscaling:EC2_INSTANCE_LAUNCHING lifecycle hook on the Auto Scaling group. Modify the application to complete the lifecycle hook when the application is ready to serve requests.

D : Increase the maximum instance count of the Auto Scaling group. Configure an autoscaling:EC2_INSTANCE_LAUNCHING lifecycle hook on the Auto Scaling group. Modify the application to complete the lifecycle hook and to place the new instance in the Standby state when the application is ready to serve requests.

Answer: A

Question 5

A company is developing a serverless application that uses AWS Lambda, AWS SAM, and Amazon API Gateway. There is a requirement to fully automate the backend Lambda deployment in such a way that the deployment will automatically run whenever a new commit is pushed to an AWS CodeCommit repository. There should also be a separate environment pipeline for TEST and PROD environments. In addition, the TEST environment should be the only one that allows automatic deployment.

As a DevOps Engineer, who can you satisfy these requirements?

Options :

A : Set up two pipelines using AWS CodePipeline for TEST and PROD environments. On both pipelines, set up a manual approval step for application deployment. Set up separate CodeCommit repositories for each environment and configure each pipeline to retrieve the source code from CodeCommit. Deploy the Lambda functions with AWS CloudFormation by setting up a deployment step in the pipeline.

B : Set up two pipelines using AWS CodePipeline for TEST and PROD environments. Configure the PROD pipeline to have a manual approval step. Create a CodeCommit repository with a branch for each environment and configure the pipeline to retrieve the source code from CodeCommit according to its branch. Deploy the Lambda functions with AWS CloudFormation by setting up a deployment step in the pipeline.

C : Set up two pipelines using AWS CodePipeline for TEST and PROD environments. In the PROD pipeline, set up a manual approval step for application deployment. Set up separate CodeCommit repositories for each environment and configure each pipeline to retrieve the source code from CodeCommit. Deploy the Lambda functions with AWS CloudFormation by setting up a deployment step in the pipeline.

D : Create a new pipeline using AWS CodePipeline and a new CodeCommit repository for each environment. Configure CodePipeline to retrieve the application source code from the appropriate repository. Deploy the Lambda functions with AWS CloudFormation by creating a deployment step in the pipeline.

Answer: B

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