Expert in Oracle PL SQL Training Crash Course
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Elevate Academy is a recognized leader in DevOps training, offering a comprehensive program designed to equip you with the skills needed to thrive in today’s cloud-driven tech landscape. This course blends deep theoretical understanding with practical, hands-on experience, preparing you to implement, manage, and scale DevOps practices using AWS services. Whether you’re beginning your journey or aiming to level up, this training ensures you’re industry-ready.
This program is part of a curated collection of industry-recommended courses selected by top companies to train and upskill their teams.
The AWS Cloud – DevOps Engineer Professional (DOP-C01) course is your gateway to mastering cloud-native DevOps on one of the world’s leading platforms. With expert instruction, hands-on labs, and project-based learning, you’ll gain the skills to build, deploy, and manage secure, scalable applications in dynamic cloud environments.
The AWS Cloud – DevOps Engineer Professional (DOP-C01) course is designed to equip learners with the comprehensive knowledge and hands-on skills required to implement modern DevOps practices on the AWS platform. Throughout the training, participants will master the core concepts of Continuous Integration and Continuous Deployment (CI/CD), infrastructure automation, and Infrastructure as Code (IaC) using AWS CloudFormation. The course also covers containerization technologies, real-time monitoring, and robust logging practices to ensure system reliability and performance. Additionally, learners will gain insight into implementing secure and compliant DevOps workflows, making them proficient in building scalable, efficient, and secure cloud-based applications.
Implement CI/CD Pipelines
Software development lifecycle (SDLC) concepts, phases, and models
Pipeline deployment patterns for single- and multi-account environments
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)
Integrate Automated Testing into CI/CD Pipelines
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
Running builds or tests when generating pull requests or code merges (for example, AWS CodeCommit, CodeBuild)
Running load/stress tests, performance benchmarking, and application testing at scale
Measuring application health based on application exit codes
Automating unit tests and code coverage
Invoking AWS services in a pipeline for testing
Build and Manage Artifacts
Artifact use cases and secure management
Methods to create and generate artifacts
Artifact lifecycle considerations
Creating and configuring artifact repositories (for example, AWS CodeArtifact, Amazon S3, Amazon Elastic Container Registry [Amazon ECR])
Configuring build tools for generating artifacts (for example, CodeBuild, AWS Lambda)
Automating Amazon EC2 instance and container image build processes (for example, EC2 Image Builder)
Deployment Strategies
Deployment methodologies for various platforms (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)
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)
Troubleshooting deployment issues
Using different deployment methods (for example, blue/green, canary)
Cloud Infrastructure and Reusable Components
Infrastructure as code (IaC) options and tools for AWS
Change management processes for IaC-based platforms
Configuration management services and strategies
Composing and deploying IaC templates (for example, AWS Serverless Application Model [AWS SAM], AWS CloudFormation, AWS Cloud Development Kit [AWS CDK])
Applying CloudFormation Stack Sets 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)
Deploy Automation
AWS account structures, best practices, and related AWS services
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)
Design and Build Automated Solutions
AWS services and solutions to automate tasks and processes
Methods and strategies to interact with the AWS software-defined infrastructure
Automating system inventory, configuration, and patch management (for example, Systems Manager, AWS Config)
Developing Lambda function automations for complex scenarios (for example, AWS SDKs, Lambda, AWS Step Functions)
Automating the configuration of software applications to the desired state (for example, OpsWorks, Systems Manager State Manager)
Maintaining software compliance (for example, Systems Manager)
Highly Available Solutions to Meet Resilience
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)
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 Regions while minimizing downtime
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 Regions for global scalability
Configuring serverless applications (for example, Amazon API Gateway, Lambda, AWS Fargate)
Automated Recovery Processes
Disaster recovery concepts (for example, RTO, RPO)
Backup and recovery strategies (for example, pilot light, warm standby)
Recovery procedures
Testing failover of Multi-AZ and 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
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)
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
Associating CloudWatch alarms with CloudWatch metrics (standard and custom)
Configuring AWS X-Ray for different services (for example, containers, API Gateway, Lambda)
Analyzing real-time log streams (for example, using Kinesis Data Streams)
Analyzing logs with AWS services (for example, Amazon Athena, CloudWatch Logs Insights)
Monitoring and Event Management
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 for 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, ALB 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 S3 events to process log files (for example, by using Lambda) and deliver log files to another destination (for example, OpenSearch Service, CloudWatch Logs)
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, ALB)
Monitoring and Logging
AWS services that generate, capture, and store logs (for example, CloudWatch, CloudTrail, AWS Health, EventBridge)
Event-driven architectures (for example, fan out, event streaming, queuing)
Integrating AWS event sources (for example, AWS Health, EventBridge, CloudTrail)
Building event processing workflows (for example, Amazon Simple Queue Service [Amazon SQS], Kinesis, Amazon SNS, Lambda, Step Functions)
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)
Identity and Access Management at Scale
Appropriate usage of different IAM entities (for example, users, groups, roles, identity providers, identity-based policies, resource-based policies, session policies) for human and machine access
Identity federation techniques (for example, using IAM identity providers and AWS IAM Identity Center [AWS Single Sign-On])
Permission management delegation by using IAM permissions boundaries
Organizational SCPs
Designing policies to enforce least privilege access
Implementing role-based and attribute-based access control patterns
Automating credential rotation for machine identities (for example, Secrets Manager)
Managing permissions to control access to human and machine identities (for example, enabling multi-factor authentication [MFA], AWS Security Token Service [AWS STS], IAM profiles)
Automation for Security Controls and Data Protection
Network security components (for example, security groups, network ACLs, routing, AWS Network Firewall, AWS WAF, AWS Shield)
Certificates and public key infrastructure (PKI)
Data management (for example, data classification, encryption, key management, access controls)
Automating the application of security controls in multi-account and multi-Region environments (for example, Security Hub, Organizations, AWS Control Tower, Systems Manager)
Combining security controls to apply defense in depth (for example, AWS Certificate Manager [ACM], AWS WAF, 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)
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)
Common cloud security threats (for example, insecure web traffic, exposed AWS access keys, S3 buckets with public access enabled or encryption disabled)
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
Understand core DevOps concepts and learn how to implement them effectively within the AWS ecosystem to accelerate software development and delivery.
Build and manage robust continuous integration and deployment (CI/CD) pipelines using AWS services such as CodePipeline, CodeBuild, and CodeDeploy for seamless automation.
Master the automation of AWS resource provisioning and management using CloudFormation, enabling scalable and repeatable infrastructure deployment.
Develop proficiency in AWS-native tools like CloudWatch and X-Ray to implement real-time monitoring, diagnostics, and logging for improved system performance and reliability.
Integrate security into every stage of your DevOps lifecycle using AWS best practices. Learn to manage identity, encryption, and compliance to ensure secure operations.
Apply your skills through immersive labs and real-world projects, equipping you to handle enterprise-level DevOps scenarios on the AWS cloud platform.
June 13, 2025In today’s fast-paced digital world, the demand for skilled software professionals is higher than ever. Whether you’re a student aiming…
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