Amazon Web Services DOP-C02 - AWS Certified DevOps Engineer - Professional

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Total 392 questions

A company is testing a web application that runs on Amazon EC2 instances behind an Application Load Balancer. The instances run in an Auto Scaling group across multiple Availability Zones. The company uses a blue green deployment process with immutable instances when deploying new software.

During testing users are being automatically logged out of the application at random times. Testers also report that when a new version of the application is deployed all users are logged out. The development team needs a solution to ensure users remain logged m across scaling events and application deployments.

What is the MOST operationally efficient way to ensure users remain logged in?

Enable smart sessions on the load balancer and modify the application to check tor an existing session.

Enable session sharing on the toad balancer and modify the application to read from the session store.

Store user session information in an Amazon S3 bucket and modify the application to read session information from the bucket.

Modify the application to store user session information in an Amazon ElastiCache cluster.

Question # 62

A company has developed a serverless web application that is hosted on AWS. The application consists of Amazon S3. Amazon API Gateway, several AWS Lambda functions, and an Amazon RDS for MySQL database. The company is using AWS CodeCommit to store the source code. The source code is a combination of AWS Serverless Application Model (AWS SAM) templates and Python code.

A security audit and penetration test reveal that user names and passwords for authentication to the database are hardcoded within CodeCommit repositories. A DevOps engineer must implement a solution to automatically detect and prevent hardcoded secrets.

What is the MOST secure solution that meets these requirements?

Enable Amazon CodeGuru Profiler. Decorate the handler function with @with_lambda_profiler(). Manually review the recommendation report. Write the secret to AWS Systems Manager Parameter Store as a secure string. Update the SAM templates and the Python code to pull the secret from Parameter Store.

Associate the CodeCommit repository with Amazon CodeGuru Reviewer. Manually check the code review for any recommendations. Choose the option to protect the secret. Update the SAM templates and the Python code to pull the secret from AWS Secrets Manager.

Enable Amazon CodeGuru Profiler. Decorate the handler function with @with_lambda_profiler(). Manually review the recommendation report. Choose the option to protect the secret. Update the SAM templates and the Python code to pull the secret from AWS Secrets Manager.

Associate the CodeCommit repository with Amazon CodeGuru Reviewer. Manually check the code review for any recommendations. Write the secret to AWS Systems Manager Parameter Store as a string. Update the SAM templates and the Python code to pull the secret from Parameter Store.

Question # 63

A company uses Amazon EC2 as its primary compute platform. A DevOps team wants to audit the company's EC2 instances to check whether any prohibited applications have been installed on the EC2 instances.

Which solution will meet these requirements with the MOST operational efficiency?

Configure AWS Systems Manager on each instance Use AWS Systems Manager Inventory Use Systems Manager resource data sync to synchronize and store findings in an Amazon S3 bucket Create an AWS Lambda function that runs when new objects are added to the S3 bucket. Configure the Lambda function to identify prohibited applications.

Configure AWS Systems Manager on each instance Use Systems Manager Inventory Create AWS Config rules that monitor changes from Systems Manager Inventory to identify prohibited applications.

Configure AWS Systems Manager on each instance. Use Systems Manager Inventory. Filter a trail in AWS CloudTrail for Systems Manager Inventory events to identify prohibited applications.

Designate Amazon CloudWatch Logs as the log destination for all application instances Run an automated script across all instances to create an inventory of installed applications Configure the script to forward the results to CloudWatch Logs Create a CloudWatch alarm that uses filter patterns to search log data to identify prohibited applications.

Explanation:

ï‚· Configure AWS Systems Manager on Each Instance:

AWS Systems Manager provides a unified interface for managing AWS resources. Install the Systems Manager agent on each EC2 instance to enable inventory management and other features.

ï‚· Use AWS Systems Manager Inventory:

Systems Manager Inventory collects metadata about your instances and the software installed on them. This data includes information about applications, network configurations, and more.

Enable Systems Manager Inventory on all EC2 instances to gather detailed information about installed applications.

ï‚· Use Systems Manager Resource Data Sync to Synchronize and Store Findings in an Amazon S3 Bucket:

Resource Data Sync aggregates inventory data from multiple accounts and regions into a single S3 bucket, making it easier to query and analyze the data.

Configure Resource Data Sync to automatically transfer inventory data to an S3 bucket for centralized storage.

ï‚· Create an AWS Lambda Function that Runs When New Objects are Added to the S3 Bucket:

Use an S3 event to trigger a Lambda function whenever new inventory data is added to the S3 bucket.

The Lambda function can parse the inventory data and check for the presence of prohibited applications.

ï‚· Configure the Lambda Function to Identify Prohibited Applications:

The Lambda function should be programmed to scan the inventory data for any known prohibited applications and generate alerts or take appropriate actions if such applications are found.

Example Lambda function in Python

import json

import boto3

def lambda_handler(event, context):

s3 = boto3.client('s3')

bucket = event['Records'][0]['s3']['bucket']['name']

key = event['Records'][0]['s3']['object']['key']

response = s3.get_object(Bucket=bucket, Key=key)

inventory_data = json.loads(response['Body'].read().decode('utf-8'))

prohibited_apps = ['app1', 'app2']

for instance in inventory_data['Instances']:

for app in instance['Applications']:

if app['Name'] in prohibited_apps:

# Send notification or take action

print(f"Prohibited application found: {app['Name']} on instance {instance['InstanceId']}")

return {'statusCode': 200, 'body': json.dumps('Check completed')}

By leveraging AWS Systems Manager Inventory, Resource Data Sync, and Lambda, this solution provides an efficient and automated way to audit EC2 instances for prohibited applications.

[References:, AWS Systems Manager Inventory, AWS Systems Manager Resource Data Sync, S3 Event Notifications, AWS Lambda, , , ]

Question # 64

A DevOps engineer is setting up an Amazon Elastic Container Service (Amazon ECS) blue/green deployment for an application by using AWS CodeDeploy and AWS CloudFormation. During the deployment window, the application must be highly available and CodeDeploy must shift 10% of traffic to a new version of the application every minute until all traffic is shifted.

Which configuration should the DevOps engineer add in the CloudFormation template to meet these requirements?

Add an AppSpec file with the CodeDeployDefault.ECSLineaMOPercentEverylMinutes deployment configuration.

Add the AWS::CodeDeployBlueGreen transform and the AWS::CodeDeploy::BlueGreen hook parameter with the CodeDeployDefault.ECSLinear10PercentEvery1 Minutes deployment configuration.

Add an AppSpec file with the ECSCanary10Percent5Minutes deployment configuration.

Add the AWS::CodeDeployBlueGroen transform and the AWS::CodeDeploy::BlueGreen hook parameter with the ECSCanary10Percent5Minutes deployment configuration.

Question # 65

A company uses AWS Organizations to manage multiple AWS accounts. The accounts are in an OU that has a policy attached to allow all actions. The company is migrating several Git repositories to a specified AWS CodeConnections supported Git provider. The Git repositories manage AWS CloudFormation stacks for application infrastructure that the company deploys across multiple AWS Regions. The company wants a DevOps team to integrate CodeConnections into the CloudFormation stacks. The DevOps team must ensure that company staff members can integrate only with the specified Git provider. The deployment process must be highly available across Regions. Which combination of steps will meet these requirements? (Select THREE.)

Add a new SCP statement to the OU that denies the CodeConnections CreatingConnections action where the provider type is not the specified Git provider.

Add a new SCP statement to the OU that allows the CodeConnections CreatingConnections action where the provider type is the specified Git provider.

Use CodeConnections to configure a single CodeConnections connection to each Git repository.

Use CodeConnections to create a CodeConnections connection from each Region where the company operates to each Git repository.

Use CodeConnections to create a CodeConnections repository link. Update each CloudFormation stack to sync from the Git repository.

For each Git repository, create a pipeline in AWS CodePipeline that has the Git repository set as the source and a CloudFormation deployment stage.

Question # 66

A DevOps engineer is setting up a container-based architecture. The engineer has decided to use AWS CloudFormation to automatically provision an Amazon ECS cluster and an Amazon EC2 Auto Scaling group to launch the EC2 container instances. After successfully creating the CloudFormation stack, the engineer noticed that, even though the ECS cluster and the EC2 instances were created successfully and the stack finished the creation, the EC2 instances were associating with a different cluster.

How should the DevOps engineer update the CloudFormation template to resolve this issue?

Reference the EC2 instances in the AWS: ECS: Cluster resource and reference the ECS cluster in the AWS: ECS: Service resource.

Reference the ECS cluster in the AWS: AutoScaling: LaunchConfiguration resource of the UserData property.

Reference the ECS cluster in the AWS:EC2: lnstance resource of the UserData property.

Reference the ECS cluster in the AWS: CloudFormation: CustomResource resource to trigger an AWS Lambda function that registers the EC2 instances with the appropriate ECS cluster.

Question # 67

A company's application uses a fleet of Amazon EC2 On-Demand Instances to analyze and process data. The EC2 instances are in an Auto Scaling group. The Auto Scaling group is a target group for an Application Load Balancer (ALB). The application analyzes critical data that cannot tolerate interruption. The application also analyzes noncritical data that can withstand interruption.

The critical data analysis requires quick scalability in response to real-time application demand. The noncritical data analysis involves memory consumption. A DevOps engineer must implement a solution that reduces scale-out latency for the critical data. The solution also must process the noncritical data.

Which combination of steps will meet these requirements? (Select TWO.)

For the critical data, modify the existing Auto Scaling group. Create a warm pool instance in the stopped state. Define the warm pool size. Create a new version of the launch template that has detailed monitoring enabled. use Spot Instances.

For the critical data. modify the existing Auto Scaling group. Create a lifecycle hook to ensure that bootstrap scripts are completed successfully. Ensure that the application on the instances is ready to accept traffic before the instances are registered. Create a new version of the launch template that has detailed monitoring enabled.

For the noncritical data, create a second Auto Scaling group that uses a launch template. Configure the launch template to install the unified Amazon CloudWatch agent and to configure the CloudWatch agent with a custom memory utilization metric. Use Spot Instances. Add the new Auto Scaling group as the target group for the ALB. Modify the application to use two target groups for critical data and noncritical data.

For the noncritical data, create a second Auto Scaling group. Choose the predefined memory utilization metric type for the target tracking scaling policy. Use Spot Instances. Add the new Auto Scaling group as the target group for the ALB. Modify the application to use two target groups for critical data and noncritical data.

Question # 68

A company is storing 100 GB of log data in csv format in an Amazon S3 bucket SQL developers want to query this data and generate graphs to visualize it. The SQL developers also need an efficient automated way to store metadata from the csv file.

Which combination of steps will meet these requirements with the LEAST amount of effort? (Select THREE.)

Fitter the data through AWS X-Ray to visualize the data.

Filter the data through Amazon QuickSight to visualize the data.

Query the data with Amazon Athena.

Query the data with Amazon Redshift.

Use the AWS Glue Data Catalog as the persistent metadata store.

Use Amazon DynamoDB as the persistent metadata store.

Question # 69

A company uses a trunk-based development branching strategy. The company has two AWS CodePipeline pipelines that are integrated with a Git provider. The pull_request pipeline has a branch filter that matches the feature branches. The main_branch pipeline has a branch filter that matches the main branch.

When pull requests are merged into the main branch, the pull requests are deployed by using the main_branch pipeline. The company's developers need test results for all submitted pull requests as quickly as possible from the pull_request pipeline. The company wants to ensure that the main_branch pipeline's test results finish and that each deployment is complete before the next pipeline execution.

Which solution will meet these requirements?

Configure the pull_request pipeline to use SUPERSEDED mode. Configure the main_branch pipeline to use QUEUED mode.

Configure the pull_request pipeline to use PARALLEL mode. Configure the main_branch pipeline to use QUEUED mode.

Configure the pull_request pipeline to use PARALLEL mode. Configure the main_branch pipeline to use SUPERSEDED mode.

Configure the pull_request pipeline to use QUEUED mode. Configure the main_branch pipeline to use SUPERSEDED mode.

Question # 70

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.)

Attach the Cloud WatchAgentServer Pol icy managed 1AM policy to the 1AM instance profile that the cluster uses.

Attach the Cloud WatchAgentServer Pol icy managed 1AM policy to a service account role for the cluster.

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.

Collect performance logs by deploying the AWS Distro for OpenTelemetry collector as a DaemonSet.

Analyze the pod_memory_utilization Amazon CloudWatch metric in the Containerlnsights namespace by using the Service dimension.

Analyze the node_memory_utilization Amazon CloudWatch metric in the Containerlnsights namespace by using the ClusterName dimension.

Explanation:

ï‚· Step 1: Attaching the CloudWatchAgentServerPolicy to the IAM Role

The CloudWatch agent needs permissions to collect and send metrics, including memory metrics, to Amazon CloudWatch. You can attach the CloudWatchAgentServerPolicy managed IAM policy to the IAM instance profile or service account role to grant these permissions.

Action: Attach the CloudWatchAgentServerPolicy managed IAM policy to the IAM instance profile that the EKS cluster uses.

Why: This ensures the CloudWatch agent has the necessary permissions to collect memory metrics.

[Reference: AWS documentation on CloudWatch Agent Permissions., This corresponds to Option A: Attach the CloudWatchAgentServerPolicy managed IAM policy to the IAM instance profile that the cluster uses., ï‚· Step 2: Deploying the CloudWatch Agent to EC2 InstancesTo collect memory metrics from the EC2 instances running in the EKS cluster, the CloudWatch agent needs to be deployed on these instances. The agent collects system-level metrics, including memory usage., Action: Deploy the unified Amazon CloudWatch agent to the existing EC2 instances in the EKS cluster. Update the Amazon Machine Image (AMI) for future instances to include the CloudWatch agent., Why: The CloudWatch agent allows you to collect detailed memory metrics from the EC2 instances, which is not enabled by default., Reference: AWS documentation on Installing and Configuring the CloudWatch Agent., This corresponds to Option 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., ï‚· Step 3: Analyzing Memory Metrics Using Container InsightsAfter collecting the memory metrics, you can analyze them using the pod_memory_utilization metric in Amazon CloudWatch Container Insights. This metric provides visibility into the memory usage of the containers (pods) in the EKS cluster., Action: Analyze the pod_memory_utilization CloudWatch metric in the Container Insights namespace by using the Service dimension., Why: This provides detailed insights into memory usage at the container level, which helps diagnose memory-related issues., Reference: AWS documentation on CloudWatch Container Insights., This corresponds to Option E: Analyze the pod_memory_utilization Amazon CloudWatch metric in the Container Insights namespace by using the Service dimension., , , ]

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Amazon Web Services DOP-C02 - AWS Certified DevOps Engineer - Professional

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