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Amazon DOP-C02 Exam Syllabus Topics:

Section	Weight	Objectives
Resilient Cloud Solutions	15%	- Design high availability architectures 1. Implement auto scaling and load balancing 2. Use Route 53 for routing and failover 3. Multi-AZ and multi-region deployments - Ensure reliability and scalability 1. Manage capacity and elasticity 2. Design for fault tolerance 3. Optimize performance and cost - Implement disaster recovery 1. Pilot light, warm standby, and active-active 2. Test and validate recovery plans 3. Backup and restore strategies
Incident and Event Response	14%	- Improve operational processes 1. Optimize response time and efficiency 2. Implement feedback loops 3. Update runbooks and playbooks - Manage incidents and outages 1. Coordinate response and recovery 2. Document and learn from events 3. Troubleshoot complex issues - Automate event management 1. Classify and prioritize events 2. Automate common operational tasks 3. Integrate with ticketing and workflow systems
Monitoring and Logging	15%	- Implement monitoring solutions 1. Create dashboards and insights 2. Monitor infrastructure, applications, and costs 3. Use Amazon CloudWatch for metrics and alarms - Implement logging and analysis 1. Analyze logs for troubleshooting and security 2. Centralize logs with CloudWatch Logs 3. Use AWS X-Ray for tracing - Automate incident response 1. Automate remediation and scaling 2. Set up event-driven actions with Amazon EventBridge 3. Implement alerting and notification workflows
Security, Governance, and Compliance	17%	- Enforce governance and compliance 1. Manage access and permissions at scale 2. Use AWS Config and AWS Organizations 3. Implement compliance rules and audits - Automate security and compliance 1. Use AWS Security Hub and AWS Shield 2. Integrate security into CI/CD pipelines 3. Detect and remediate violations automatically - Implement security controls 1. Encrypt data at rest and in transit 2. Secure secrets and credentials 3. Manage IAM for pipelines and environments
Configuration Management and Infrastructure as Code	17%	- Automate configuration management 1. Use AWS Systems Manager, Ansible, or Puppet 2. Maintain consistent configurations 3. Detect and remediate configuration drift - Implement infrastructure as code 1. Enforce configuration standards 2. Manage templates and stacks 3. Use AWS CloudFormation, Terraform, or AWS CDK - Manage infrastructure deployments 1. Manage multi-account and multi-region environments 2. Provision and update resources automatically 3. Implement version control for configurations
SDLC Automation	22%	- Implement CI/CD pipelines 1. Integrate version control systems 2. Automate build, test, and deployment stages 3. Use AWS CodePipeline, CodeBuild, CodeDeploy - Automate software release processes 1. Automate rollback procedures 2. Manage artifacts and dependencies 3. Implement deployment strategies - Automate testing and validation 1. Unit, integration, and performance testing 2. Security and compliance testing 3. Automate quality gates

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Amazon AWS Certified DevOps Engineer - Professional Sample Questions (Q190-Q195):

NEW QUESTION # 190
A company has developed an AWS Lambda function that handles orders received through an API. The company is using AWS CodeDeploy to deploy the Lambda function as the final stage of a CI/CD pipeline.
A DevOps engineer has noticed there are intermittent failures of the ordering API for a few seconds after deployment. After some investigation the DevOps engineer believes the failures are due to database changes not having fully propagated before the Lambda function is invoked How should the DevOps engineer overcome this?

A. Add a BeforeAllowTraffic hook to the AppSpec file that tests and waits for any necessary database changes before deploying the new version of the Lambda function.
B. Add a BeforeAllowTraffic hook to the AppSpec file that tests and waits for any necessary database changes before traffic can flow to the new version of the Lambda function.
C. Add a validateService hook to the AppSpec file that inspects incoming traffic and rejects the payload if dependent services such as the database are not yet ready.
D. Add an AfterAlIowTraffic hook to the AppSpec file that forces traffic to wait for any pending database changes before allowing the new version of the Lambda function to respond.

Answer: B

Explanation:
https://docs.aws.amazon.com/codedeploy/latest/userguide/reference-appspec-file-structure-hooks.html#appspec-hooks-lambda

NEW QUESTION # 191
A company has a workflow that generates a file for each of the company ' s products and stores the files in a production environment Amazon S3 bucket. The company ' s users can access the S3 bucket.
Each file contains a product ID. Product IDs for products that have not been publicly announced are prefixed with a specific UUID. Product IDs are 12 characters long. IDs for products that have not been publicly announced begin with the letter P.
The company does not want information about products that have not been publicly announced to be available in the production environment S3 bucket.
Which solution will meet these requirements?

A. Create a new staging S3 bucket. Generate all files in the new staging bucket. Create an Amazon Macie custom data identifier to identify product IDs in the new bucket that begin with the specific UUID.
Launch an Amazon Macie sensitive data discovery job with the custom data identifier. Copy all files that do not have a Macie finding to the production S3 bucket.
B. Create a new staging S3 bucket. Generate all files in the new staging bucket. Launch an Amazon Macie sensitive data discovery job with a managed data identifier. Copy all files that do not have a Macie finding to the production S3 bucket.
C. Create an Amazon Macie custom data identifier to identify product IDs in the production bucket that begin with the specific UUID. Launch an Amazon Macie sensitive data discovery job with the custom data identifier. Remove all files that have a Macie finding from the production S3 bucket.
D. Create an Amazon Macie sensitive data discovery job with a managed data identifier. Remove all files that have a Macie finding from the production S3 bucket.

Answer: A

Explanation:
The requirement is to prevent any product files containing unannounced product IDs (prefixed with a specific UUID) from being stored in the production S3 bucket that users can access.
To achieve this, a best practice is to use a staging bucket as a control point before files go to production, combined with Amazon Macie's data classification capabilities.
* Creating a custom data identifier in Amazon Macie allows precise detection of product IDs starting with the specific UUID, which default managed identifiers will not detect.
* By running a Macie sensitive data discovery job on the staging bucket, you can identify files containing these sensitive product IDs.
* Only files without findings (i.e., files that do not contain unannounced product IDs) are copied to the production bucket, ensuring no sensitive information is exposed. This approach aligns with AWS best practices for data classification and staged deployment workflows, maximizing control and reducing risk. Using Macie on the production bucket directly (options B and D) risks exposing sensitive data before detection and deletion. Option C uses managed data identifiers, which will likely not detect the custom UUID prefix pattern.
Reference from AWS Official Documentation and Study Guide:
* Amazon Macie Custom Data Identifiers: " You can create custom data identifiers in Amazon Macie to find sensitive data that is unique to your organization. " (Amazon Macie User Guide)
* Data Security Best Practices: " Use staging environments to inspect and sanitize data before moving it to production to reduce exposure risks. " (AWS Security Best Practices)

NEW QUESTION # 192
A company recently deployed its web application on AWS. The company is preparing for a large-scale sales event and must ensure that the web application can scale to meet the demand The application's frontend infrastructure includes an Amazon CloudFront distribution that has an Amazon S3 bucket as an origin. The backend infrastructure includes an Amazon API Gateway API. several AWS Lambda functions, and an Amazon Aurora DB cluster The company's DevOps engineer conducts a load test and identifies that the Lambda functions can fulfill the peak number of requests However, the DevOps engineer notices request latency during the initial burst of requests Most of the requests to the Lambda functions produce queries to the database A large portion of the invocation time is used to establish database connections Which combination of steps will provide the application with the required scalability? (Select TWO)

A. Configure a higher provisioned concurrency for the Lambda functions
B. Use Amazon RDS Proxy to create a proxy for the Aurora database Update the Lambda functions to use the proxy endpoints for database connections.
C. Refactor the Lambda Functions Move the code blocks that initialize database connections into the function handlers.
D. Convert the DB cluster to an Aurora global database Add additional Aurora Replicas in AWS Regions based on the locations of the company's customers.
E. Configure a higher reserved concurrency for the Lambda functions.

Answer: A,B

Explanation:
The correct answer is B and E. Configuring a higher provisioned concurrency for the Lambda functions will ensure that the functions are ready to respond to the initial burst of requests without any cold start latency.
Using Amazon RDS Proxy to create a proxy for the Aurora database will enable the Lambda functions to reuse existing database connections and reduce the overhead of establishing new ones. This will also improve the scalability and availability of the database by managing the connection pool size and handling failovers.
Option A is incorrect because reserved concurrency only limits the number of concurrent executions for a function, not pre-warms them. Option C is incorrect because converting the DB cluster to an Aurora global database will not address the issue of database connection latency, and may introduce additional costs and complexity. Option D is incorrect because moving the code blocks that initialize database connections into the function handlers will not improve the performance or scalability of the Lambda functions, and may actually worsen the cold start latency. References:
* AWS Lambda Provisioned Concurrency
* Using Amazon RDS Proxy with AWS Lambda
* Certified DevOps Engineer - Professional (DOP-C02) Study Guide (page 173)

NEW QUESTION # 193
A DevOps engineer has implemented a Cl/CO pipeline to deploy an AWS Cloud Format ion template that provisions a web application. The web application consists of an Application Load Balancer (ALB) a target group, a launch template that uses an Amazon Linux 2 AMI an Auto Scaling group of Amazon EC2 instances, a security group and an Amazon RDS for MySQL database The launch template includes user data that specifies a script to install and start the application.
The initial deployment of the application was successful. The DevOps engineer made changes to update the version of the application with the user data. The CI/CD pipeline has deployed a new version of the template However, the health checks on the ALB are now failing The health checks have marked all targets as unhealthy.
During investigation the DevOps engineer notices that the Cloud Formation stack has a status of UPDATE_COMPLETE. However, when the DevOps engineer connects to one of the EC2 instances and checks /varar/log messages, the DevOps engineer notices that the Apache web server failed to start successfully because of a configuration error How can the DevOps engineer ensure that the CloudFormation deployment will fail if the user data fails to successfully finish running?

A. Use the cfn-signal helper script to signal success or failure to CloudFormation Use the WaitOnResourceSignals update policy within the CloudFormation template Set an appropriate timeout for the update policy.
B. Use the Amazon CloudWatch agent to stream the cloud-init logs Create a subscription filter that includes an AWS Lambda function with an appropriate invocation timeout Configure the Lambda function to use the SignalResource API operation to signal success or failure to CloudFormation.
C. Create an Amazon CloudWatch alarm for the UnhealthyHostCount metric. Include an appropriate alarm threshold for the target group Create an Amazon Simple Notification Service (Amazon SNS) topic as the target to signal success or failure to CloudFormation
D. Create a lifecycle hook on the Auto Scaling group by using the AWS AutoScaling LifecycleHook resource Create an Amazon Simple Notification Service (Amazon SNS) topic as the target to signal success or failure to CloudFormation Set an appropriate timeout on the lifecycle hook.

Answer: A

Explanation:
Explanation
https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-attribute-updatepolicy.html

NEW QUESTION # 194
A company uses AWS Control Tower and Organizations for a multi-account environment. It needs to create new accounts and ensure they receive a consistent baseline configuration.
Which solution meets the requirement with the least overhead?

A. Use Account Factory Customization (AFC) blueprints for baseline setup.
B. Use Organizations + StackSets manually.
C. Use Organizations with Lambda applying baseline via access role.
D. Use Account Factory + StackSets post-setup.

Answer: A

Explanation:
Account Factory Customization (AFC) automates Control Tower account provisioning with baseline blueprints for networking, logging, and guardrails. It integrates natively and minimizes overhead, per AWS Control Tower documentation.

NEW QUESTION # 195
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