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Amazon DOP-C02 Exam Syllabus Topics:

SectionWeightObjectives
Incident and Event Response14%- Manage incidents and outages
  • 1. Coordinate response and recovery
  • 2. Document and learn from events
  • 3. Troubleshoot complex issues
- Improve operational processes
  • 1. Update runbooks and playbooks
  • 2. Optimize response time and efficiency
  • 3. Implement feedback loops
- Automate event management
  • 1. Integrate with ticketing and workflow systems
  • 2. Classify and prioritize events
  • 3. Automate common operational tasks
SDLC Automation22%- Automate testing and validation
  • 1. Automate quality gates
  • 2. Unit, integration, and performance testing
  • 3. Security and compliance testing
- Automate software release processes
  • 1. Automate rollback procedures
  • 2. Implement deployment strategies
  • 3. Manage artifacts and dependencies
- Implement CI/CD pipelines
  • 1. Use AWS CodePipeline, CodeBuild, CodeDeploy
  • 2. Automate build, test, and deployment stages
  • 3. Integrate version control systems
Security, Governance, and Compliance17%- Implement security controls
  • 1. Manage IAM for pipelines and environments
  • 2. Encrypt data at rest and in transit
  • 3. Secure secrets and credentials
- Automate security and compliance
  • 1. Detect and remediate violations automatically
  • 2. Integrate security into CI/CD pipelines
  • 3. Use AWS Security Hub and AWS Shield
- Enforce governance and compliance
  • 1. Implement compliance rules and audits
  • 2. Manage access and permissions at scale
  • 3. Use AWS Config and AWS Organizations
Configuration Management and Infrastructure as Code17%- Implement infrastructure as code
  • 1. Use AWS CloudFormation, Terraform, or AWS CDK
  • 2. Enforce configuration standards
  • 3. Manage templates and stacks
- Automate configuration management
  • 1. Detect and remediate configuration drift
  • 2. Maintain consistent configurations
  • 3. Use AWS Systems Manager, Ansible, or Puppet
- Manage infrastructure deployments
  • 1. Manage multi-account and multi-region environments
  • 2. Provision and update resources automatically
  • 3. Implement version control for configurations
Resilient Cloud Solutions15%- Implement disaster recovery
  • 1. Test and validate recovery plans
  • 2. Backup and restore strategies
  • 3. Pilot light, warm standby, and active-active
- Design high availability architectures
  • 1. Implement auto scaling and load balancing
  • 2. Multi-AZ and multi-region deployments
  • 3. Use Route 53 for routing and failover
- Ensure reliability and scalability
  • 1. Design for fault tolerance
  • 2. Manage capacity and elasticity
  • 3. Optimize performance and cost
Monitoring and Logging15%- Implement logging and analysis
  • 1. Centralize logs with CloudWatch Logs
  • 2. Analyze logs for troubleshooting and security
  • 3. Use AWS X-Ray for tracing
- Automate incident response
  • 1. Implement alerting and notification workflows
  • 2. Set up event-driven actions with Amazon EventBridge
  • 3. Automate remediation and scaling
- Implement monitoring solutions
  • 1. Monitor infrastructure, applications, and costs
  • 2. Use Amazon CloudWatch for metrics and alarms
  • 3. Create dashboards and insights

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Amazon AWS Certified DevOps Engineer - Professional Sample Questions (Q78-Q83):

NEW QUESTION # 78
A cloud team uses AWS Organizations and AWS IAM Identity Center to manage a company ' s AWS accounts. The company recently established a research team. The research team requires the ability to fully manage the resources in its account. The research team must not be able to create IAM users.
The cloud team creates a Research Administrator permission set in IAM Identity Center for the research team.
The permission set has the AdministratorAccess AWS managed policy attached. The cloud team must ensure that no one on the research team can create IAM users.
Which solution will meet these requirements?

Answer: B

Explanation:
Service Control Policies in AWS Organizations set the maximum permissions available to all identities within an account, including users who have administrator-level permission sets in IAM Identity Center. A Deny SCP for iam:CreateUser attached to the research team ' s account overrides the AdministratorAccess policy in the permission set and cannot be bypassed by any action within the account. This makes it the most enforceable and tamper-resistant solution. Option A attaches a deny policy to the permission set itself, which would work for sessions using that permission set, but does not prevent IAM users in the account from creating other IAM users. Option B sets a permissions boundary, which can only limit a role ' s maximum permissions, not prevent users from performing actions outside their own role ' s context. Option D is reactive, allowing IAM user creation before deletion, which is not prevention.


NEW QUESTION # 79
A large enterprise is deploying a web application on AWS. The application runs on Amazon EC2 instances behind an Application Load Balancer. The instances run in an Auto Scaling group across multiple Availability Zones. The application stores data in an Amazon RDS for Oracle DB instance and Amazon DynamoDB. There are separate environments tor development testing and production.
What is the MOST secure and flexible way to obtain password credentials during deployment?

Answer: A

Explanation:
AWS Secrets Manager is a secrets management service that helps you protect access to your applications, services, and IT resources. This service enables you to easily rotate, manage, and retrieve database credentials, API keys, and other secrets throughout their lifecycle. Using Secrets Manager, you can secure and manage secrets used to access resources in the AWS Cloud, on third-party services, and on-premises. SSM parameter store and AWS Secret manager are both a secure option. However, Secrets manager is more flexible and has more options like password generation. Reference: https://www.1strategy.com/blog/2019/02/28/aws-parameter-store-vs-aws-secrets-manager/


NEW QUESTION # 80
A company is using AWS CodePipeline to automate its release pipeline. AWS CodeDeploy is being used in the pipeline to deploy an application to Amazon Elastic Container Service (Amazon ECS) using the blue/green deployment model. The company wants to implement scripts to test the green version of the application before shifting traffic. These scripts will complete in 5 minutes or less. If errors are discovered during these tests, the application must be rolled back.
Which strategy will meet these requirements?

Answer: C

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


NEW QUESTION # 81
A DevOps team supports an application that sends many requests through multiple external systems. The application runs on many Amazon EC2 instances in an Auto Scaling group. The application stages requests to the external systems in Amazon Simple Queue Service (Amazon SQS) queues. The application emits logs to Amazon CloudWatch Logs.
The DevOps team wants to notify an Amazon Simple Notification Service (Amazon SNS) topic when there are 10 or more errors during a 5-minute period for requests to the external systems.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: C

Explanation:
Amazon CloudWatch metric filters are the least-overhead method for converting log data into actionable metrics and alarms. By defining a metric filter on the CloudWatch Logs log group with a regex pattern that matches external system error log entries, CloudWatch automatically increments a custom metric each time a matching event appears. A CloudWatch alarm configured with a period of 5 minutes and a threshold of 10 will trigger and publish directly to the SNS topic without requiring any additional compute resources. Option B adds an unnecessary Lambda invocation between the alarm and the SNS notification. Option C uses anomaly detection, which is probabilistic and not suited for a hard threshold requirement. Option D requires a subscription filter plus a Lambda function to publish the custom metric, which introduces additional components, Lambda costs, and higher operational complexity than a native metric filter.


NEW QUESTION # 82
A company has an application that runs on AWS Lambda and sends logs to Amazon CloudWatch Logs. An Amazon Kinesis data stream is subscribed to the log groups in CloudWatch Logs. A single consumer Lambda function processes the logs from the data stream and stores the logs in an Amazon S3 bucket.
The company's DevOps team has noticed high latency during the processing and ingestion of some logs.
Which combination of steps will reduce the latency? (Select THREE.)

Answer: C,D,F

Explanation:
Explanation
The latency in processing and ingesting logs can be caused by several factors, such as the throughput of the Kinesis data stream, the concurrency of the Lambda function, and the configuration of the event source mapping. To reduce the latency, the following steps can be taken:
* Create a data stream consumer with enhanced fan-out. Set the Lambda function that processes the logs as the consumer. This will allow the Lambda function to receive records from the data stream with dedicated throughput of up to 2 MB per second per shard, independent of other consumers1. This will reduce the contention and delay in accessing the data stream.
* Increase the ParallelizationFactor setting in the Lambda event source mapping. This will allow the Lambda service to invoke more instances of the function concurrently to process the records from the data stream2. This will increase the processing capacity and reduce the backlog of records in the data stream.
* Configure reserved concurrency for the Lambda function that processes the logs. This will ensure that the function has enough concurrency available to handle the increased load from the data stream3. This will prevent the function from being throttled by the account-level concurrency limit.
The other options are not effective or may have negative impacts on the latency. Option D is not suitable because increasing the batch size in the Kinesis data stream will increase the amount of data that the Lambda function has to process in each invocation, which may increase the execution time and latency4. Option E is not advisable because turning off the ReportBatchItemFailures setting in the Lambda event source mapping will prevent the Lambda service from retrying the failed records, which may result in data loss. Option F is not necessary because increasing the number of shards in the Kinesis data stream will increase the throughput of the data stream, but it will not affect the processing speed of the Lambda function, which is the bottleneck in this scenario.
References:
* 1: Using AWS Lambda with Amazon Kinesis Data Streams - AWS Lambda
* 2: AWS Lambda event source mappings - AWS Lambda
* 3: Managing concurrency for a Lambda function - AWS Lambda
* 4: AWS Lambda function scaling - AWS Lambda
* : AWS Lambda event source mappings - AWS Lambda
* : Scaling Amazon Kinesis Data Streams with AWS CloudFormation - Amazon Kinesis Data Streams


NEW QUESTION # 83
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