Exam Name: AWS Certified DevOps Engineer - Professional (DOP-C01 Korean Version)
Certification Provider: Amazon
Corresponding Certification: AWS Certified DevOps Engineer
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AWS-DevOps Exam Syllabus Topics:
| Section | Objectives |
|---|---|
SDLC Automation - 22% | |
| Apply concepts required to automate a CI/CD pipeline | - Set up repositories - Set up build services - Integrate automated testing (e.g., unit tests, integrity tests) - Set up deployment products/services - Orchestrate multiple pipeline stages |
| Determine source control strategies and how to implement them | - Determine a workflow for integrating code changes from multiple contributors - Assess security requirements and recommend code repository access design - Reconcile running application versions to repository versions (tags) - Differentiate different source control types |
| Apply concepts required to automate and integrate testing | - Run integration tests as part of code merge process - Run load/stress testing and benchmark applications at scale - Measure application health based on application exit codes (robust Health Check) - Automate unit tests to check pass/fail, code coverage
- Integrate tests with pipeline |
| Apply concepts required to build and manage artifacts securely | - Distinguish storage options based on artifacts security classification - Translate application requirements into Operating System and package configuration (build specs) - Determine the code/environment dependencies and required resources
- Run a code build process |
| Determine deployment/delivery strategies (e.g., A/B, Blue/green, Canary, Red/black) and how to implement them using AWS services | - Determine the correct delivery strategy based on business needs - Critique existing deployment strategies and suggest improvements - Recommend DNS/routing strategies (e.g., Route 53, ELB, ALB, load balancer) based on business continuity goals - Verify deployment success/failure and automate rollbacks |
Configuration Management and Infrastructure as Code - 19% | |
| Determine deployment services based on deployment needs | - Demonstrate knowledge of process flows of deployment models - Given a specific deployment model, classify and implement relevant AWS services to meet requirements
|
| Determine application and infrastructure deployment models based on business needs | - Balance different considerations (cost, availability, time to recovery) based on business requirements to choose the best deployment model - Determine a deployment model given specific AWS services - Analyze risks associated with deployment models and relevant remedies |
| Apply security concepts in the automation of resource provisioning | - Choose the best automation tool given requirements - Demonstrate knowledge of security best practices for resource provisioning (e.g., encrypting data bags, generating credentials on the fly) - Review IAM policies and assess if sufficient but least privilege is granted for all lifecycle stages of a deployment (e.g., create, update, promote) - Review credential management solutions (e.g., EC2 parameter store, third party) - Build the automation
|
| Determine how to implement lifecycle hooks on a deployment | - Determine appropriate integration techniques to meet project requirements - Choose the appropriate hook solution (e.g., implement leader node selection after a node failure) in an Auto Scaling group - Evaluate hook implementation for failure impacts (if a remote call fails, if a dependent service is temporarily unavailable (i.e., Amazon S3), and recommend resiliency improvements - Evaluate deployment rollout procedures for failure impacts and evaluate rollback/recovery processes |
| Apply concepts required to manage systems using AWS configuration management tools and services | - Identify pros and cons of AWS configuration management tools - Demonstrate knowledge of configuration management components - Show the ability to run configuration management services end to end with no assistance while adhering to industry best practices |
Monitoring and Logging - 15% | |
| Determine how to set up the aggregation, storage, and analysis of logs and metrics | - Implement and configure distributed logs collection and processing (e.g., agents, syslog, flumed, CW agent) - Aggregate logs (e.g., Amazon S3, CW Logs, intermediate systems (EMR), Kinesis FH – Transformation, ELK/BI) - Implement custom CW metrics, Log subscription filters - Manage Log storage lifecycle (e.g., CW to S3, S3 lifecycle, S3 events) |
| Apply concepts required to automate monitoring and event management of an environment | - Parse logs (e.g., Amazon S3 data events/event logs/ELB/ALB/CF access logs) and correlate with other alarms/events (e.g., CW events to AWS Lambda) and take appropriate action - Use CloudTrail/VPC flow logs for detective control (e.g., CT, CW log filters, Athena, NACL or WAF rules) and take dependent actions (AWS step) based on error handling logic (state machine) - Configure and implement Patch/inventory/state management using ESM (SSM), Inspector, CodeDeploy, OpsWorks, and CW agents
- Handle scaling/failover events (e.g., ASG, DB HA, route table/DNS update, Application Config, Auto Recovery, PH dashboard, TA) |
| Apply concepts required to audit, log, and monitor operating systems, infrastructures, and applications | - Monitor end to end service metrics (DDB/S3) using available AWS tools (X-ray with EB and Lambda) - Verify environment/OS state through auditing (Inspector), Config rules, CloudTrail (process and action), and AWS APIs - Enable, configure, and analyze custom metrics (e.g., Application metrics, memory, KCL/KPL) and take action - Ensure container monitoring (e.g., task state, placement, logging, port mapping, LB) - Distinguish between services that enable service level or OS level monitoring
|
| Determine how to implement tagging and other metadata strategies | - Segregate authority based on tagging (lifecycle stages – dev/prod) with Condition context keys - Utilize Amazon S3 system/user-defined metadata for classification and automation - Design and implement tag-based deployment groups with CodeDeploy - Best practice for cost allocation/optimization with tagging |
Policies and Standards Automation - 10% | |
| Apply concepts required to enforce standards for logging, metrics, monitoring, testing, and security | - Detect, report, and respond to governance and security violations - Apply logging standards across application, operating system, and infrastructure - Apply context specific application health and performance monitoring - Outline standards for delivery models for logs and metrics (e.g., JSON, XML, Data Normalization) |
| Determine how to optimize cost through automation | - Prioritize automation effort to reduce labor costs - Implement right sizing of workload based on metrics - Assess ways to improve time to market through automating process orchestration and repeatable tasks - Diagnose outliers to determine use case fit
- Measure and automate cost optimization through events
|
| Apply concepts required to implement governance strategies | - Generalize governance standards across CI/CD pipeline - Outline and measure the real-time status of compliance with governance strategies - Report on compliance with governance strategies - Deploy governance policies related to self-service capabilities
|
Incident and Event Response - 18% | |
| Troubleshoot issues and determine how to restore operations | - Given an issue, evaluate how to narrow down the unhealthy components as quickly as possible - Given an increase in load, determine what steps to take to mitigate the impact - Determine the causes and impacts of a failure
- Determine the best way to restore operations after a failure occurs
|
| Determine how to automate event management and alerting | - Set up automated restores from backup in the event of a catastrophic failure - Set up methods to deliver alerts and notifications that are appropriate for different types of events - Assess the quality/actionability of alerts - Configure metrics appropriate to an application’s SLAs - Proactively update limits |
| Apply concepts required to implement automated healing | - Set up the correct scaling strategy to enable auto-healing when a failure occurs (e.g., with Auto Scaling policies) - Use the correct rollback strategy to avoid impact from failed deployments - Configure Route 53 to ensure cross-Region failover - Detect and respond to maintenance or Spot termination events |
| Apply concepts required to set up event-driven automated actions | - Configure Lambda functions or CloudWatch actions to implement automated actions - Set up CloudWatch event rules and/or Config rules and targets - Use AWS Systems Manager or Step Functions to coordinate components (e.g., Lambda, use maintenance windows) - Configure a build/roll-out process to automatically respond to critical software updates |
High Availability, Fault Tolerance, and Disaster Recovery - 16% | |
| Determine appropriate use of multi-AZ versus multi-Region architectures | - Determine deployment strategy based on HA/DR requirements - Determine data replication strategy based on cost and durability requirements - Determine infrastructure, platform, and services based on HA/DR requirements - Design for HA/FT/DR based on service availability (i.e., global/regional/single AZ) |
| Determine how to implement high availability, scalability, and fault tolerance | - Design deployment strategy to support HA/FT/scalability - Assess statefulness of application infrastructure components - Use load balancing to distribute traffic across multiple AZ/ASGs/instance types (spot/M4 vs C4) /targets - Use appropriate caching solutions to improve availability and performance |
| Determine the right services based on business needs (e.g., RTO/RPO, cost) | - Determine cost-effective storage solution for your application
- Choose a database platform and configuration to meet business requirements
- Choose a deployment service/model based on business requirements
- Determine when to use managed service vs. self-managed infrastructure (Docker on EC2 vs. ECS) |
| Determine how to design and automate disaster recovery strategies | - Automate failure detection - Automate components/environment recovery - Choose appropriate deployment strategy for environment recovery - Design automation to support failover in hybrid environment |
| Evaluate a deployment for points of failure | - Determine appropriate deployment-specific health checks - Implement failure detection during deployment - Implement failure event handling/response - Ensure that resources/components/processes exist to react to failures during deployment - Look for exit codes on each event of the deployment - Map errors to different points of deployment |
The AWS Certified DevOps Engineer – Professional or DOP-C01 exam focuses on DevOps engineers, developers, or any other specialist who is familiar with working with AWS software architecture.
Reference: https://aws.amazon.com/certification/certified-devops-engineer-professional/
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