AWS Certified DevOps Engineer – Professional Course in Dubai

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)

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, 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

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 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)

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)

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 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)

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)

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)

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 Zones and Regions while minimizing downtime

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)

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

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)

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 [ALB])

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)

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)

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

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)

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

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)

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 IAM Identity Center)

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)

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 defence 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)

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