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Amazon DOP-C02 Exam Syllabus Topics:

Section	Weight	Objectives
Incident and Event Response	18%	- Design and implement event and incident management 1. Implement automated incident detection 2. Implement automated response playbooks 3. Design event aggregation and correlation - Design and implement chaos engineering practices 1. Analyze system behavior under failure conditions 2. Implement fault injection experiments (AWS Fault Injection Simulator) 3. Design resilience testing strategies
Configuration Management and Infrastructure as Code	22%	- Design and implement configuration management 1. Implement parameter management (AWS Parameter Store, Secrets Manager) 2. Design patch management strategies 3. Implement AWS Systems Manager for configuration management - Implement compliance and configuration monitoring 1. Implement AWS CloudTrail for auditing 2. Use AWS Config for compliance monitoring 3. Design remediation automation - Design and implement data management strategies 1. Implement database migration strategies 2. Design backup and recovery solutions 3. Implement data lifecycle management - Design and implement infrastructure as code 1. Develop IaC templates (AWS CloudFormation, Terraform) 2. Design for scalability and repeatability 3. Implement modular and reusable infrastructure components
Monitoring and Logging	12%	- Design and implement alerting and incident management 1. Implement automated incident response 2. Design runbook automation 3. Create alarm notification strategies - Design and implement monitoring and observability strategies 1. Design custom metrics and alarms (Amazon CloudWatch) 2. Implement log aggregation and analysis 3. Implement distributed tracing (AWS X-Ray)
High Availability and Disaster Recovery	16%	- Implement data backup and restore strategies 1. Implement validation testing for backups 2. Design point-in-time recovery solutions 3. Implement cross-region replication - Design and implement high availability and scalability 1. Implement load balancing and traffic management 2. Design multi-AZ and multi-region architectures 3. Implement auto scaling strategies - Design and implement disaster recovery strategies 1. Implement backup and restore mechanisms 2. Design RTO and RPO based DR solutions 3. Implement multi-region active-active architectures 4. Implement pilot light and warm standby architectures
SDLC Automation	22%	- Design build and test environments 1. Integrate security scanning and compliance checks 2. Design test automation frameworks 3. Implement build environments (isolated, reproducible) - Design and implement CI/CD pipelines 1. Determine appropriate CI/CD pipeline architecture 2. Develop CI/CD pipelines considering testing and security requirements 3. Design failure handling strategies 4. Implement deployment strategies (blue-green, canary, rolling) - Design and implement source code management strategies 1. Design code review and approval processes 2. Determine branching strategies 3. Implement repository configurations and hooks
Policies and Standards Automation	10%	- Design and implement governance strategies 1. Implement approval workflows and automation 2. Implement tagging policies and resource grouping 3. Design cost optimization through policies - Design and implement preventive and detective controls 1. Design and implement security baselines 2. Implement drift detection and remediation 3. Implement AWS Organizations and SCPs

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Amazon AWS Certified DevOps Engineer - Professional Sample Questions (Q233-Q238):

NEW QUESTION # 233
A company uses a series of individual Amazon Cloud Formation templates to deploy its multi-Region Applications. These templates must be deployed in a specific order. The company is making more changes to the templates than previously expected and wants to deploy new templates more efficiently. Additionally, the data engineering team must be notified of all changes to the templates.
What should the company do to accomplish these goals?

A. Leverage CloudFormation nested stacks and stack sets (or deployments Use Amazon SNS to notify the data engineering team.
B. Host the Cloud Formation templates in Amazon S3 Use Amazon S3 events to directly trigger CloudFormation updates and Amazon SNS notifications.
C. Create an AWS Lambda function to deploy the Cloud Formation templates m the required order Use stack policies to alert the data engineering team.
D. Implement CloudFormation StackSets and use drift detection to trigger update alerts to the data engineering team.

Answer: A

Explanation:
This solution will meet the requirements because it will use CloudFormation nested stacks and stack sets to deploy the templates more efficiently and consistently across multiple regions. Nested stacks allow the company to separate out common components and reuse templates, while stack sets allow the company to create stacks in multiple accounts and regions with a single template. The company can also use Amazon SNS to send notifications to the data engineering team whenever a change is made to the templates or the stacks. Amazon SNS is a service that allows you to publish messages to subscribers, such as email addresses, phone numbers, or other AWS services. By using Amazon SNS, the company can ensure that the data engineering team is aware of all changes to the templates and can take appropriate actions if needed. What is Amazon SNS? - Amazon Simple Notification Service

NEW QUESTION # 234
A DevOps engineer is building a solution that uses Amazon Simple Queue Service (Amazon SQS) standard queues. The solution also includes an AWS Lambda function and an Amazon DynamoDB table. The Lambda function pulls content from the SQS queue and writes the content to the DynamoDB table.
The solution must maximize the scalability of the Lambda function and must prevent successfully processed SQS messages from being processed multiple times.
Which solution will meet these requirements?

A. Set the batch size to 1 in the Lambda function ' s event source mapping.
B. Set the reserved concurrency to 1 in the Lambda function ' s concurrency and recursion detection settings.
C. Set the batch window to 1 second in the Lambda function ' s event source mapping.
D. Set a value of ReportBatchItemFailures in the FunctionResponseTypes list in the Lambda function ' s event source mapping.

Answer: D

Explanation:
When a Lambda function processes an SQS batch and a subset of messages fail, the default behavior re- queues the entire batch, causing already-successfully-processed messages to be delivered and processed again.
The ReportBatchItemFailures feature in the Lambda event source mapping allows the function to return a partial failure response, indicating only the specific message IDs that failed. Lambda then deletes the successfully processed messages from the queue and re-queues only the failed items, preventing duplicate processing of successful messages. This approach maintains high concurrency because it does not restrict parallelism. Setting batch size to 1 (Option B) avoids batching entirely but eliminates the concurrency and throughput benefits. Reserved concurrency of 1 (Option D) severely restricts scalability. A 1-second batch window (Option A) affects how long Lambda waits to form a batch but does not prevent duplicate processing.

NEW QUESTION # 235
A video-sharing company stores its videos in Amazon S3. The company has observed a sudden increase in video access requests, but the company does not know which videos are most popular. The company needs to identify the general access pattern for the video files. This pattern includes the number of users who access a certain file on a given day, as well as the numbA 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'' er of pull requests for certain files.
How can the company meet these requirements with the LEAST amount of effort?

A. Activate S3 server access logging. Import the access logs into an Amazon Aurora database. Use an Aurora SQL query to analyze the access patterns.
B. Invoke an AWS Lambda function for every S3 object access event. Configure the Lambda function to write the file access information, such as user. S3 bucket, and file key, to an Amazon Aurora database.
Use an Aurora SQL query to analyze the access patterns.
C. Activate S3 server access logging. Use Amazon Athena to create an external table with the log files.
Use Athena to create a SQL query to analyze the access patterns.
D. Record an Amazon CloudWatch Logs log message for every S3 object access event. Configure a CloudWatch Logs log stream to write the file access information, such as user, S3 bucket, and file key, to an Amazon Kinesis Data Analytics for SQL application. Perform a sliding window analysis.

Answer: C

Explanation:
Activating S3 server access logging and using Amazon Athena to create an external table with the log files is the easiest and most cost-effective way to analyze access patterns. This option requires minimal setup and allows for quick analysis of theaccess patterns with SQL queries. Additionally, Amazon Athena scales automatically to match the query load, so there is no need for additional infrastructure provisioning or management.

NEW QUESTION # 236
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?

A. 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.
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 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.
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: C

Explanation:
Option A is the most cost-effective solution. By configuring a warm pool of EC2 instances in the Stopped state, the company can reduce the time it takes for new instances to be ready to serve requests. When the Auto Scaling group launches a new instance, it can attach the stopped EC2 instance from the warm pool. The instance can then be started up immediately, rather than having to wait for the data to be downloaded and processed. This reduces the overall startup time for the application.

NEW QUESTION # 237
A company is hosting a web application in an AWS Region. For disaster recovery purposes, a second region is being used as a standby. Disaster recovery requirements state that session data must be replicated between regions in near-real time and 1% of requests should route to the secondary region to continuously verify system functionality. Additionally, if there is a disruption in service in the main region, traffic should be automatically routed to the secondary region, and the secondary region must be able to scale up to handle all traffic.
How should a DevOps engineer meet these requirements?

A. In both regions, launch the application in Auto Scaling groups and use DynamoDB global tables for session data. Enable an Amazon CloudFront weighted distribution across regions. Point the Amazon Route 53 DNS record at the CloudFront distribution.
B. In both regions, deploy the application in AWS Lambda, exposed by Amazon API Gateway, and use Amazon RDS for PostgreSQL with cross-region replication for session data. Deploy the web application with client-side logic to call the API Gateway directly.
C. In both regions, launch the application in Auto Scaling groups and use DynamoDB for session data. Use a Route 53 failover routing policy with health checks to distribute the traffic across the regions.
D. In both regions, deploy the application on AWS Elastic Beanstalk and use Amazon DynamoDB global tables for session data. Use an Amazon Route 53 weighted routing policy with health checks to distribute the traffic across the regions.

Answer: A

NEW QUESTION # 238
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