AWS Certified DevOps Engineer – Professional (DOP-C02)

Best AWS certified devops engineer Course

The AWS Certified DevOps Engineer – Professional (DOP-C02) is a high-level certification that validates expertise in automating infrastructure, monitoring systems, managing CI/CD pipelines, and building secure, scalable operations on AWS. This course is designed for experienced DevOps professionals who want to master the tools, services, and processes that enable faster and more efficient software delivery using AWS. 

At Linux Training Center in Coimbatore, our specialized training program offers real-world DevOps scenarios, hands-on labs, and detailed guidance to help learners become job-ready and certification-ready. 

Whether it’s managing large-scale deployments with infrastructure as code, implementing automated testing workflows, or setting up monitoring and logging for production environments, this course prepares you to take full control of your DevOps lifecycle in the AWS ecosystem. With a curriculum aligned to the latest DOP-C02 exam objectives, you’ll gain confidence to work in high-availability, fast-paced cloud environments that demand operational excellence.

Who Should Enroll?

This course is ideal for DevOps engineers, cloud automation specialists, system administrators, release engineers, and software developers working in AWS environments who are looking to step into advanced DevOps roles. It’s also well-suited for professionals who hold the AWS Certified Developer – Associate or AWS Certified SysOps Administrator – Associate certifications and want to upgrade to the professional level.

What You Will Learn

You will learn to automate code deployments using AWS CodePipeline, CodeDeploy, and CodeBuild, manage infrastructure as code with tools like AWS CloudFormation and Terraform, monitor system health with CloudWatch and X-Ray, build scalable and resilient architectures using ECS, Lambda, and Auto Scaling, ensure compliance and governance with IAM and policies, and design continuous delivery workflows that align with DevSecOps and reliability engineering principles. Through project-based learning, you’ll also explore disaster recovery, performance tuning, and cost control within real AWS environments.

Prerequisites

A solid understanding of core AWS services and DevOps principles is required. Prior hands-on experience with Linux, scripting, CI/CD tools, containerization, and cloud monitoring will be highly beneficial. While not mandatory, having an associate-level AWS certification is strongly recommended before taking this course.

Course Benefits

Complete DOP-C02 exam coverage with scenario-based training, live projects with automation and pipeline configurations, full access to practice exams and lab guides, instructor-led sessions by AWS Certified DevOps Experts, and post-course job support with resume preparation and mock interviews.

Career Opportunities After Certification

This certification qualifies you for advanced roles such as AWS DevOps Engineer, Site Reliability Engineer, Cloud Automation Architect, Infrastructure as Code Specialist, and Cloud Operations Manager.

Join our AWS Certified DevOps Engineer – Professional (DOP-C02) course at Linux Training Center, Coimbatore, and elevate your career in cloud automation and DevOps engineering. With expert training, real-time labs, and focused exam preparation, we help you become a certified DevOps professional that top cloud employers are seeking. 

Limited seats available – contact us now and secure your spot in the next batch.

AWS Certified DevOps Engineer – Professional Syllabus

Chapter 1

Course Introduction, What is DevOps, Assumed Knowledge

Chapter 2

SDLC Automation, Introduction, What is CI/CD?, AWS CodeCommit, AWS CodeBuild, AWS CodeDeploy, AWS CodePipeline, Testing, Artifacts, Deployment Strategies, HANDS-ON LAB Creating an AWS CodeCommit Repository That Triggers Email Notifications, HANDS-ON LAB Configure and Work with CodeCommit from the CLI, HANDS-ON LAB Setting Up an AWS CodePipeline with a Manual Approval

Implement CI/CD pipelines.

Knowledge of:

Software development lifecycle (SDLC) concepts, phases, and models

Pipeline deployment patterns for single- and multi-account environments

Skills in:

Configuring code, image, and artifact repositories
Using version control to integrate pipelines with application environments

Setting up build processes (for example, AWS CodeBuild)
Managing build and deployment secrets (for example, AWS Secrets Manager, AWS Systems Manager Parameter Store)

Determining appropriate deployment strategies (for example, AWS CodeDeploy)

Chapter 3

Configuration Management and Infrastructure as Code, Introduction, AWS CloudFormation, AWS CloudFormation Lab, AWS CloudFormation Intrinsic Functions, AWS CloudFormation Wait Conditions, AWS CloudFormation Nested Stacks, AWS CloudFormation Deletion Policies, AWS CloudFormation Stack Updates, AWS CloudFormation Change Sets, AWS CloudFormation Custom Resources, AWS CloudFormation Custom Resources Lab, AWS Elastic Beanstalk, AWS Elastic Beanstalk Lab, AWS Elastic Beanstalk ebextensions, AWS Config, AWS Config Lab, Amazon ECS, Amazon ECS Lab, AWS Managed Services, AWS Lambda, AWS Lambda Lab, AWS Lambda Step Functions, AWS OpsWorks, AWS OpsWorks Lab, HANDS-ON LAB Working with CloudFormation Condition Functions, HANDS-ON LAB Working with CloudFormation Nested Stacks, HANDS-ON LAB Updating CloudFormation Stacks with Direct Updates and Change Sets

Integrate automated testing into CI/CD pipelines.

Knowledge of:

Different types of tests (for example, unit tests, integration tests, acceptance tests, user interface tests, security scans)

Reasonable use of different types of tests at different stages of the CI/CD pipeline

Chapter 4

Monitoring and Logging, Introduction, CloudWatch Overview, CloudWatch Lab, CloudWatch Custom Metrics, CloudWatch Events Lab, CloudWatch Logs Lab, AWS X-Ray and Lab, HANDS-ON LAB Monitoring AWS CodePipeline Changes Through AWS CloudWatch Events Rules

Build and manage artifacts

Knowledge of:

Artifact use cases and secure management

Methods to create and generate artifacts
Artifact lifecycle considerations

Chapter 5

Policies and Standards Automation, Introduction, AWS Service Catalog, AWS Trusted Advisor, AWS Systems Manager, AWS Organizations, AWS Secrets Manager, Amazon Macie, AWS Certificate Manager

Implement deployment strategies for instance, container, and serverless environments

Knowledge of:

Deployment methodologies for various platforms (for example, Amazon EC2, Amazon Elastic Container Service [Amazon ECS], Amazon Elastic Kubernetes Service [Amazon EKS], Lambda)

Application storage patterns (for example, Amazon Elastic File System [Amazon EFS], Amazon S3, Amazon Elastic Block Store [Amazon EBS])

Mutable deployment patterns in contrast to immutable deployment patterns
Tools and services available for distributing code (for example, CodeDeploy, EC2 Image Builder)

Skills in:

Configuring security permissions to allow access to artifact repositories (for example, AWS Identity and Access Management [IAM], CodeArtifact)

Configuring deployment agents (for example, CodeDeploy agent)

roubleshooting deployment issues
Using different deployment methods (for example, blue/green, canary)

Chapter 6

Incident and Event Response, Introduction, Amazon GuardDuty, Amazon Inspector, Amazon Kinesis

Define cloud infrastructure and reusable components to provision and manage systems throughout their lifecycle.

Knowledge of:

Infrastructure as code (IaC) options and tools for AWS

Change management processes for IaC-based platforms

Configurations management services and strategies

Skills in:

Composing and deploying IaC templates (for example, AWS Serverless Application Model [AWS SAM], AWS CloudFormation, AWS Cloud Development Kit [AWS CDK])
Applying AWS CloudFormation StackSets across multiple accounts and AWS Regions

Determining optimal configuration management services (for example, AWS OpsWorks, AWS Systems Manager, AWS Config, AWS AppConfig)

Implementing infrastructure patterns, governance controls, and security standards into reusable IaC templates (for example, AWS Service Catalog, CloudFormation modules, AWS CDK)

Chapter 7

High Availability, Fault Tolerance and Disaster Recovery, Introduction, AWS Single Sign-On, Amazon CloudFront, AutoScaling and Lifecycle hooks, Amazon Route53, Amazon RDS, Amazon Aurora, Amazon DynamoDB, Amazon DynamoDB Keys and Streams, HANDS-ON LAB Deploying an EC2 Instance Using Cross-Stack References

Deploy automation to create, onboard, and secure AWS accounts in a multi account/multi-Region environment.

Knowledge of:

AWS account structures, best practices, and related AWS services

Skills in:

Standardizing and automating account provisioning and configuration

Creating, consolidating, and centrally managing accounts (for example, AWS Organizations, AWS Control Tower)
Applying IAM solutions for multi-account and complex organization structures (for example, SCPs, assuming roles)

Implementing and developing governance and security controls at scale (AWS Config, AWS Control Tower, AWS Security Hub, Amazon Detective, Amazon GuardDuty, AWS Service Catalog, SCPs)

Chapter 8

Other Services You Need to Know About, Introduction, Tagging, Amazon Elastic File System, Amazon ElastiCache, Amazon S3 Glacier, AWS Direct Connect, AWS Lambda Function Dead Letter Queues, Amazon CloudSearch, Amazon Elasticsearch Service, Amazon DynamoDB Accelerator, AWS Server Migration Service

Design and build automated solutions for complex tasks and large-scale environments

Knowledge of:

AWS services and solutions to automate tasks and processes

Methods and strategies to interact with the AWS software-defined infrastructure

Chapter 9

Implement highly available solutions to meet resilience and business requirements.

Knowledge of:

Multi-AZ and multi-Region deployments (for example, compute layer, data layer)
SLAs

Replication and failover methods for stateful services
Techniques to achieve high availability (for example, Multi-AZ, multi-Region)

Skills in:

Translating business requirements into technical resiliency needs

Identifying and remediating single points of failure in existing workloads

Enabling cross-Region solutions where available (for example, Amazon DynamoDB, Amazon RDS, Amazon Route 53, Amazon S3, Amazon CloudFront)

Configuring load balancing to support cross-AZ services,
Configuring applications and related services to support multiple Availability Zones and Regions while minimizing downtime

Chapter 10

Implement solutions that are scalable to meet business requirements.

Appropriate metrics for scaling services

Loosely coupled and distributed architectures

Serverless architectures

Container platforms

Identifying and remediating scaling issues

Identifying and implementing appropriate auto scaling, load balancing, and caching solutions

Deploying container-based applications (for example, Amazon ECS, Amazon EKS)

Deploying workloads in multiple AWS Regions for global scalability
Configuring serverless applications (for example, Amazon API Gateway, Lambda, AWS Fargate)

Implement automated recovery processes to meet RTO/RPO requirements.

Disaster recovery concepts (for example, RTO, RPO)
Backup and recovery strategies (for example, pilot light, warm standby)

Recovery procedures

Testing failover of Multi-AZ/multi-Region workloads (for example, Amazon RDS, Amazon Aurora, Route 53, CloudFront)

Identifying and implementing appropriate cross-Region backup and recovery strategies (for example, AWS Backup, Amazon S3, Systems Manager)

Configuring a load balancer to recover from backend failure

Chapter 11

Monitoring and Logging

Configure the collection, aggregation, and storage of logs and metrics.

How to monitor applications and infrastructure
Amazon CloudWatch metrics (for example, namespaces, metrics, dimensions, and resolution)

Real-time log ingestion
Encryption options for at-rest and in-transit logs and metrics (for example, client-side and server-side, AWS Key Management Service [AWS KMS])

Security configurations (for example, IAM roles and permissions to allow for log collection)

Securely storing and managing logs

Creating CloudWatch metrics from log events by using metric filters

Creating CloudWatch metric streams (for example, Amazon S3 or Amazon Kinesis Data Firehose options)
Collecting custom metrics (for example, using the CloudWatch agent)

Managing log storage lifecycles (for example, S3 lifecycles, CloudWatch log group retention)
Processing log data by using CloudWatch log subscriptions (for example, Kinesis, Lambda, Amazon OpenSearch Service)

Searching log data by using filter and pattern syntax or CloudWatch Logs Insights

Configuring encryption of log data (for example, AWS KMS)

Chapter 12

Audit, monitor, and analyze logs and metrics to detect issues.

Anomaly detection alarms (for example, CloudWatch anomaly detection)

Common CloudWatch metrics and logs (for example, CPU utilization with Amazon EC2, queue length with Amazon RDS, 5xx errors with an Application Load Balancer)

Amazon Inspector and common assessment templates

AWS Config rules,
AWS CloudTrail log events

Building CloudWatch dashboards and Amazon QuickSight visualizations

Automate monitoring and event management of complex environments.

Event-driven, asynchronous design patterns (for example, S3 Event Notifications or Amazon EventBridge events to Amazon Simple Notification Service [Amazon SNS] or Lambda)

Capabilities of auto scaling a variety of AWS services (for example, EC2 Auto Scaling groups, RDS storage auto scaling, DynamoDB, ECS capacity provider, EKS autoscalers)

Alert notification and action capabilities (for example, CloudWatch alarms to Amazon SNS, Lambda, EC2 automatic recovery)

Health check capabilities in AWS services (for example, Application Load Balancer target groups, Route 53)

Configuring solutions for auto scaling (for example, DynamoDB, EC2 Auto Scaling groups, RDS storage auto scaling, ECS capacity provider)

Creating CloudWatch custom metrics and metric filters, alarms, and notifications (for example, Amazon SNS, Lambda)

Configuring EventBridge to send notifications based on a particular event pattern
Installing and configuring agents on EC2 instances (for example, AWS Systems Manager Agent [SSM Agent], CloudWatch agent)

Configuring AWS Config rules to remediate issues

Configuring health checks (for example, Route 53, Application Load Balancer)

Chapter 13

Incident and Event Response

Manage event sources to process, notify, and take action in response to events.

AWS services that generate, capture, and process events (for example, AWS Health, EventBridge, CloudTrail, CloudWatch Events)

Event-driven architectures (for example, fan out, event streaming, queuing)

Integrating AWS event sources (for example, AWS Health, EventBridge, CloudTrail, CloudWatch Events)

Building event processing workflows (for example, Amazon Simple Queue Service [Amazon SQS], Kinesis, Amazon SNS, Lambda, Step Functions)

Implement configuration changes in response to events.

Fleet management services (for example, Systems Manager, AWS Auto Scaling)

Configuration management services (for example, AWS Config)

Applying configuration changes to systems

Modifying infrastructure configurations in response to events,
Remediating a non-desired system state

Troubleshoot system and application failures.

AWS metrics and logging services (for example, CloudWatch, X-Ray)

AWS service health services (for example, AWS Health, CloudWatch, Systems Manager OpsCenter)
Root cause analysis

Analyzing failed deployments (for example, AWS CodePipeline, CodeBuild, CodeDeploy, CloudFormation, CloudWatch synthetic monitoring)

Analyzing incidents regarding failed processes (for example, auto scaling, Amazon ECS, Amazon EKS)

Chapter 14

Security and Compliance

Implement techniques for identity and access management at scale.

Appropriate usage of different IAM entities for human and machine access (for example, users, groups, roles, identity providers, identity-based policies, resource-based policies, session policies)

Identity federation techniques (for example, using IAM identity providers and AWS Single Sign-On)

Permission management delegation by using IAM

permissions boundaries

Organizational SCPs

Apply automation for security controls and data protection.

Network security components (for example, security groups, network ACLs, routing, AWS Network Firewall, AWS WAF, AWS Shield)

Combining security controls to apply defense in depth (for example, AWS Certificate Manager [ACM], AWS WAF, AWS Config, AWS Config rules, Security Hub, GuardDuty, security groups, network ACLs, Amazon Detective, Network Firewall)

Automating the discovery of sensitive data at scale (for example, Amazon Macie)

Encrypting data in transit and data at rest (for example, AWS KMS, AWS CloudHSM, ACM)

Implement security monitoring and auditing solutions.

Security auditing services and features (for example, CloudTrail, AWS Config, VPC Flow Logs, CloudFormation drift detection)

AWS services for identifying security vulnerabilities and events (for example, GuardDuty, Amazon Inspector, IAM Access Analyzer, AWS Config)

Implementing robust security auditing
Configuring alerting based on unexpected or anomalous security events

Configuring service and application logging (for example, CloudTrail, CloudWatch Logs)

Analyzing logs, metrics, and security findings

Conclusion