The AWS Certified DevOps Engineer Professional exam checks if a person can create, launch, and handle applications on the AWS platform using DevOps methods. It covers various topics such as continuous integration and delivery, using code to set up infrastructure, monitoring, logging, and security. To pass this exam, you need to know about the following areas:
- As an AWS Certified DevOps Engineer Professional, you must have a deep understanding of the principles and practices of DevOps, including continuous integration, continuous delivery, infrastructure as code, and automated testing.
- You should have a solid understanding of various AWS services and tools such as EC2, RDS, Lambda, CloudFormation, CodePipeline, and CodeDeploy. You should be able to architect and deploy complex, scalable, and highly available systems on AWS.
- You need to be good at using a scripting language like Python, Ruby, or Bash. You should also have experience with automation tools like Ansible, Puppet, or Chef. And it’s important to know how to use Git to keep track of changes.
- As a DevOps Engineer, your job is to find and fix problems in complex systems. You need to be good at figuring out issues fast and explaining what you find in a clear way to others. Also, you should be great at working with different teams to get things done together.
In this blog, we’ll look at the important ideas you need to understand to pass the AWS Certified DevOps Engineer Professional exam. We’ll also give you useful tips, suggest materials to study, and offer resources to help you prepare for the test. Whether you’re experienced in DevOps or just starting your career, this blog will give you helpful advice to become an AWS Certified DevOps Engineer Professional. Let’s begin!
Exam Objectives
The AWS Certified DevOps Engineer Professional exam is designed to test your ability to:
- Designing and managing continuous delivery systems and methodologies on AWS.
- Understand, implement, and automate security controls, governance processes, and compliance validation on AWS.
- Defining and deploying monitoring, metrics, and logging systems on AWS.
- Implement and manage AWS resource orchestration and automation using AWS CloudFormation and other tools.
- Understand and implement hybrid and multi-region architectures, and apply network design principles to meet workload requirements on AWS.
- Implement, manage, and operate scalable, highly available, and fault-tolerant systems on AWS.
Glossary for AWS Certified DevOps Engineer Professional Terminology
- Agile: A methodology that emphasizes flexibility, collaboration, and customer satisfaction in software development.
- Automation: Using technology to complete tasks without the need for humans to be involved.
- CI/CD: Continuous integration and continuous delivery/deployment are a group of methods to make software delivery faster and more dependable.
- Containerization: A way to package and run software in a portable and efficient manner.
- Deployment: The process of making software available for use by users or systems.
- DevOps: A culture and set of practices that combines software development and IT operations for faster and more reliable software delivery.
- Infrastructure as code: A practice of managing infrastructure using code to achieve consistency and reproducibility.
- ITIL: A framework for IT service management that emphasizes best practices for service delivery and customer satisfaction.
- Jenkins: An open-source automation server for building, testing, and deploying software.
- Kubernetes: An open-source container orchestration platform for managing containerized applications.
- Microservices: Creating software by breaking it into small, standalone services that can be managed and expanded individually.
- Monitoring: The practice of observing and measuring the performance and availability of software systems.
- Orchestration: The automation and coordination of complex tasks and processes.
- Pipeline: A set of stages that code goes through, from development to deployment.
- Puppet: An open-source configuration management tool for managing infrastructure as code.
- Quality assurance: The process of ensuring that software meets specified quality criteria.
- Release management: The process of planning, scheduling, and controlling the release of software.
- Scrum: An agile methodology for managing and completing complex projects.
- Service-level agreement (SLA): A contract that specifies the level of service a customer can expect from a service provider.
- Source control: The practice of managing changes to software code using version control systems.
- Testing: The process of verifying that software meets specified requirements and functions as intended.
- Toolchain: A set of software tools used to develop, build, test, and deploy software.
- Version control system (VCS): A tool for managing changes to software code over time.
- Waterfall: A traditional project management methodology that follows a linear, sequential approach.
- Zero downtime deployment: A deployment technique that ensures that users can access a system without interruption during the deployment process.
AWS Certified DevOps Engineer-Professional Study Guide
Make your thoughts free and construct a study schedule that you are most comfortable with when studying for this certification test. To get an edge in this, it is necessary to be stress-free, and focused, and to gain expertise in the AWS environment. Here is a step-by-step study plan to assist you on your way to earning your certification.
Step 1: Review the Exam Guide
The AWS Certified DevOps Engineer Professional Exam Guide is a comprehensive document that outlines the structure of the exam, the topics that will be covered, and the objectives of the exam. This guide is important for those getting ready for the exam because it tells you what to anticipate on the day of the test. Start by reading the Exam Guide thoroughly to grasp the test’s structure, which consists of multiple-choice and multiple-answer questions. It also gives you insight into how hard the exam is, how much time you have, and what score you need to pass.
Module 1: Understanding SDLC Automation (22%)
1.1: Implement CI/CD pipelines.
Required Knowledge
- Software development lifecycle (SDLC) concepts, phases, and models
- Pipeline deployment patterns for single- and multi-account environments
Skills
- Configuring code, image, and artifact repositories (AWS Documentation: AWS::CodeArtifact::Repository)
- Using version control to integrate pipelines with application environments (AWS Documentation: Integrations with CodePipeline action types)
- Setting up build processes (for example, AWS CodeBuild) (AWS Documentation: What is AWS CodeBuild?)
- Managing build and deployment secrets (for example, AWS Secrets Manager, AWS Systems Manager Parameter Store) (AWS Documentation: Referencing AWS Secrets Manager secrets from Parameter Store parameters)
- Determining appropriate deployment strategies (for example, AWS CodeDeploy) (AWS Documentation: Working with deployment configurations in CodeDeploy)
1.2: Integrate automated testing into CI/CD pipelines.
Required Knowledge
- 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
Skills
- Running builds or tests when generating pull requests or code merges (for example, AWS CodeCommit, CodeBuild) (AWS Documentation: Working with pull requests in AWS CodeCommit repositories)
- Running load/stress tests, performance benchmarking, and application testing at scale (AWS Documentation: Load testing applications)
- Measuring application health based on application exit codes (AWS Documentation: Metrics commonly used for health checks)
- Automating unit tests and code coverage (AWS Documentation: Integrating with automated tests)
- Invoking AWS services in a pipeline for testing (AWS Documentation: Invoke an AWS Lambda function in a pipeline in CodePipeline)
1.3 Build and manage artifacts.
Required Knowledge
- Artifact use cases and secure management
- Methods to create and generate artifacts
- Artifact lifecycle considerations
Skills
- Creating and configuring artifact repositories (for example, AWS CodeArtifact, Amazon S3, Amazon Elastic Container Registry [Amazon ECR]) (AWS Documentation: Create a repository)
- Configuring build tools for generating artifacts (for example, CodeBuild, AWS Lambda) (AWS Documentation: Build specification reference for CodeBuild)
- Automating Amazon EC2 instance and container image build processes (for example, EC2 Image Builder) (AWS Documentation: What is EC2 Image Builder?)
1. 4: Implement deployment strategies for instance, container, and serverless environments.
Required Knowledge
- 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
- Configuring security permissions to allow access to artifact repositories (for example, AWS Identity and Access Management [IAM], CodeArtifact) (AWS Documentation: Identity and Access Management for AWS CodeArtifact)
- Configuring deployment agents (for example, CodeDeploy agent) (AWS Documentation: Working with the CodeDeploy agent)
- Troubleshooting deployment issues (AWS Documentation: Troubleshooting CodeDeploy)
- Using different deployment methods (for example, blue/green, canary) (AWS Documentation: Blue/Green Deployments)
Module 2: Understanding Configuration Management and IaC (17%)
2.1 Define cloud infrastructure and reusable components to provision and manage systems throughout their lifecycle.
Required Knowledge
- Infrastructure as code (IaC) options and tools for AWS
- Change management processes for IaC-based platforms
- Configurations management services and strategies
Skills
- Composing and deploying IaC templates (for example, AWS Serverless Application Model [AWS SAM], AWS CloudFormation, AWS Cloud Development Kit [AWS CDK]) (AWS Documentation: What is the AWS CDK?)
- Applying AWS CloudFormation StackSets across multiple accounts and AWS Regions (AWS Documentation: Use AWS CloudFormation StackSets for Multiple Accounts in an AWS Organization)
- Determining optimal configuration management services (for example, AWS OpsWorks, AWS Systems Manager, AWS Config, AWS AppConfig) (AWS Documentation: What is AWS AppConfig?)
- Implementing infrastructure patterns, governance controls, and security standards into reusable IaC templates (for example, AWS Service Catalog, CloudFormation modules, AWS CDK) (AWS Documentation: Deploy and manage AWS Control Tower controls by using AWS CDK and AWS CloudFormation)
2.2 Deploy automation to create, onboard, and secure AWS accounts in a multiaccount/multi-Region environment.
Required Knowledge
- AWS account structures, best practices, and related AWS services
Skills
- Standardizing and automating account provisioning and configuration (AWS Documentation: Automate account creation, and resource provisioning)
- Creating, consolidating, and centrally managing accounts (for example, AWS Organizations, AWS Control Tower) (AWS Documentation: Manage Accounts Through AWS Organizations)
- Applying IAM solutions for multi-account and complex organization structures (for example, SCPs, assuming roles) (AWS Documentation: Service control policies (SCPs))
- 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 Documentation: What Is AWS Control Tower?)
2. 3: Design and build automated solutions for complex tasks and large-scale environments.
Required Knowledge
- AWS services and solutions to automate tasks and processes
- Methods and strategies to interact with the AWS software-defined infrastructure
Skills
- Automating system inventory, configuration, and patch management (for example, Systems Manager, AWS Config) (AWS Documentation: AWS Systems Manager Patch Manager)
- Developing Lambda function automations for complex scenarios (for example, AWS SDKs, Lambda, AWS Step Functions) (AWS Documentation: Getting started with Lambda)
- Automating the configuration of software applications to the desired state (for example, OpsWorks, Systems Manager State Manager) (AWS Documentation: AWS Systems Manager State Manager)
- Maintaining software compliance (for example, Systems Manager) (AWS Documentation: AWS Systems Manager Compliance)
Module 3: Understanding Resilient Cloud Solutions (15%)
3.1 Implement highly available solutions to meet resilience and business requirements.
Required Knowledge
- 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
- Translating business requirements into technical resiliency needs
- Identifying and remediating single points of failure in existing workloads (AWS Documentation: Failure management)
- Enabling cross-Region solutions where available (for example, Amazon DynamoDB, Amazon RDS, Amazon Route 53, Amazon S3, Amazon CloudFront) (AWS Documentation: Use various origins with CloudFront distributions)
- Configuring load balancing to support cross-AZ services (AWS Documentation: Cross-zone load balancing for target groups)
- Configuring applications and related services to support multiple Availability Zones and Regions while minimizing downtime (AWS Documentation: Configuring and managing a Multi-AZ deployment)
3.2 Implement solutions that are scalable to meet business requirements.
Required Knowledge
- Appropriate metrics for scaling services
- Loosely coupled and distributed architectures
- Serverless architectures
- Container platforms
Skills
- Identifying and remediating scaling issues (AWS Documentation: What is Amazon EC2 Auto Scaling?)
- Identifying and implementing appropriate auto scaling, load balancing, and caching solutions (AWS Documentation: Set up a scaled and load-balanced application)
- Deploying container-based applications (for example, Amazon ECS, Amazon EKS) (AWS Documentation: Deploy a sample application)
- Deploying workloads in multiple AWS Regions for global scalability (AWS Documentation: Deploy the workload to multiple locations)
- Configuring serverless applications (for example, Amazon API Gateway, Lambda, AWS Fargate) (AWS Documentation: Build and Test a Serverless Application with AWS Lambda)
3.3 Implement automated recovery processes to meet RTO/RPO requirements.
Required Knowledge
- Disaster recovery concepts (for example, RTO, RPO)
- Backup and recovery strategies (for example, pilot light, warm standby)
- Recovery procedures
Skills
- Testing failover of Multi-AZ/multi-Region workloads (for example, Amazon RDS, Amazon Aurora, Route 53, CloudFront) (AWS Documentation: Configuring and managing a Multi-AZ deployment)
- Identifying and implementing appropriate cross-Region backup and recovery strategies (for example, AWS Backup, Amazon S3, Systems Manager) (AWS Documentation: Amazon S3 backups)
- Configuring a load balancer to recover from backend failure (AWS Documentation: Configuring an Application Load Balancer)
Module 4: Monitoring and Logging (15%)
4.1 Configure the collection, aggregation, and storage of logs and metrics.
Required Knowledge
- 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)
Skills
- Securely storing and managing logs (AWS Documentation: What is Amazon CloudWatch Logs?)
- Creating CloudWatch metrics from log events by using metric filters (AWS Documentation: Create a metric filter for a log group)
- Creating CloudWatch metric streams (for example, Amazon S3 or Amazon Kinesis Data Firehose options) (AWS Documentation: Custom setup with Firehose)
- Collecting custom metrics (for example, using the CloudWatch agent) (AWS Documentation: Collect metrics, logs, and traces with the CloudWatch agent)
- Managing log storage lifecycles (for example, S3 lifecycles, CloudWatch log group retention) (AWS Documentation: Managing your storage lifecycle)
- Processing log data by using CloudWatch log subscriptions (for example, Kinesis, Lambda, Amazon OpenSearch Service) (AWS Documentation: Real-time processing of log data with subscriptions)
- Searching log data by using filter and pattern syntax or CloudWatch Logs Insights (AWS Documentation: Filter pattern syntax for metric filters, subscription filters, filter log events, and Live Tail)
- Configuring encryption of log data (for example, AWS KMS) (AWS Documentation: Encrypt log data in CloudWatch Logs using AWS Key Management Service)
4.2 Audit, monitor, and analyze logs and metrics to detect issues.
Required Knowledge
- 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
Skills
- Building CloudWatch dashboards and Amazon QuickSight visualizations (AWS Documentation: Monitoring data in Amazon QuickSight)
- Associating CloudWatch alarms with CloudWatch metrics (standard and custom) (AWS Documentation: Create alarms for custom metrics using Amazon CloudWatch anomaly detection)
- Configuring AWS X-Ray for different services (for example, containers, API Gateway, Lambda) (AWS Documentation: Visualize Lambda function invocations using AWS X-Ray)
- Analyzing real-time log streams (for example, using Kinesis Data Streams) (AWS Documentation: What Is Amazon Kinesis Data Streams?)
- Analyzing logs with AWS services (for example, Amazon Athena, CloudWatch Logs Insights) (AWS Documentation: Analyzing log data with CloudWatch Logs Insights)
4.3 Automate monitoring and event management of complex environments.
Required Knowledge
- 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)
Skills
- Configuring solutions for auto scaling (for example, DynamoDB, EC2 Auto Scaling groups, RDS storage auto scaling, ECS capacity provider) (AWS Documentation: Automatically manage Amazon ECS capacity with cluster auto scaling)
- Creating CloudWatch custom metrics and metric filters, alarms, and notifications (for example, Amazon SNS, Lambda) (AWS Documentation: Creating custom CloudWatch metrics and alarms in AMS)
- 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 (AWS Documentation: Log Amazon S3 object-level operations using EventBridge)
- Installing and configuring agents on EC2 instances (for example, AWS Systems Manager Agen [SSM Agent], CloudWatch agent) (AWS Documentation: Installing the CloudWatch agent using AWS Systems Manager)
- Configuring AWS Config rules to remediate issues (AWS Documentation: Remediating Noncompliant Resources with AWS Config Rules)
- Configuring health checks (for example, Route 53, Application Load Balancer) (AWS Documentation: How health checks work in simple Amazon Route 53 configurations)
Module 5: Incident and Event Response (14%)
5.1 Manage event sources to process, notify, and take action in response to events.
Required Knowledge
- 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)
Skills
- Integrating AWS event sources (for example, AWS Health, EventBridge, CloudTrail, CloudWatch Events) (AWS Documentation: Events from AWS services)
- Building event processing workflows (for example, Amazon Simple Queue Service [Amazon SQS], Kinesis, Amazon SNS, Lambda, Step Functions) (AWS Documentation: Using Lambda with Amazon SQS)
5.2 Implement configuration changes in response to events.
Required Knowledge
- Fleet management services (for example, Systems Manager, AWS Auto Scaling)
- Configuration management services (for example, AWS Config)
Skills
- Applying configuration changes to systems (AWS Documentation: What is AWS AppConfig?)
- Modifying infrastructure configurations in response to events (AWS Documentation: Example Events for AWS Config Rules)
- Remediating a non-desired system state (AWS Documentation: Remediating Noncompliant Resources with AWS Config Rules)
5.3 Troubleshoot system and application failures.
Required Knowledge
- 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
Skills
- Analyzing failed deployments (for example, AWS CodePipeline, CodeBuild, CodeDeploy, CloudFormation, CloudWatch synthetic monitoring) (AWS Documentation: Monitoring deployments with Amazon CloudWatch tools)
- Analyzing incidents regarding failed processes (for example, auto scaling, Amazon ECS, Amazon EKS) (AWS Documentation: Autoscaling)
Module 6: Security and Compliance (17%)
6.1 Implement techniques for identity and access management at scale.
Required Knowledge
- 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
Skills
- Designing policies to enforce least privilege access (AWS Documentation: Implementing policies for least-privilege permissions for AWS CloudFormation)
- Implementing role-based and attribute-based access control patterns (AWS Documentation: What is ABAC for AWS?)
- Automating credential rotation for machine identities (for example, Secrets Manager) (AWS Documentation: Automatically rotate IAM user access keys at scale with AWS Organizations and AWS 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) (AWS Documentation: Security best practices in IAM)
6.2 Apply automation for security controls and data protection.
Required Knowledge
- 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)
Skills
- Automating the application of security controls in multi-account and multi-Region environments (for example, Security Hub, Organizations, AWS Control Tower, Systems Manager) (AWS Documentation: AWS multi-account strategy for your AWS Control Tower landing zone)
- 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) (AWS Documentation: Security group policies)
- Automating the discovery of sensitive data at scale (for example, Amazon Macie) (AWS Documentation: Discovering sensitive data with Amazon Macie)
- Encrypting data in transit and data at rest (for example, AWS KMS, AWS CloudHSM, ACM) (AWS Documentation: Encrypting Data-at-Rest and Data-in-Transit)
6.3 Implement security monitoring and auditing solutions.
Required Knowledge
- 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)
Skills
- Implementing robust security auditing (AWS Documentation: AWS security audit guidelines)
- Configuring alerting based on unexpected or anomalous security events (AWS Documentation: Using CloudWatch anomaly detection)
- Configuring service and application logging (for example, CloudTrail, CloudWatch Logs) (AWS Documentation: Sending events to CloudWatch Logs)
- Analyzing logs, metrics, and security findings (AWS Documentation: Analyze logs, findings, and metrics centrally)
Step 2: Go through the AWS Learning Path
This learning route is for those who wish to learn how to create, deploy, and manage apps in the AWS Cloud using the most prevalent DevOps practices. As you work toward AWS Certification, you’ll develop technical abilities.
Step 3: Take an Instructor-led Training
The official instructor-led training for the AWS Certified DevOps Engineer Professional exam is a three-day course that provides hands-on experience with AWS services and tools commonly used in DevOps workflows. The course is led by an AWS-accredited instructor who has experience working with DevOps teams and understands the challenges involved in developing and deploying applications on AWS.
The course covers a wide range of topics related to DevOps on AWS, including:
- Continuous integration and continuous delivery (CI/CD) pipelines: The course covers how to build, test, and deploy applications using CI/CD pipelines on AWS. It covers best practices for creating scalable and reliable pipelines and explores tools like AWS CodePipeline, AWS CodeBuild, and AWS CodeDeploy.
- Infrastructure as code: The course covers how to use infrastructure as code (IaC) to manage and deploy infrastructure on AWS. It explores tools like AWS CloudFormation and AWS Elastic Beanstalk and covers best practices for creating reusable templates and managing changes to infrastructure over time.
- Monitoring and logging: The course covers how to use AWS services like Amazon CloudWatch and AWS X-Ray to monitor and troubleshoot applications running on AWS. It explores how to set up alarms and notifications, and how to use logging to diagnose and resolve issues.
- Security and compliance: The course covers best practices for securing applications and infrastructure on AWS, including how to use AWS Identity and Access Management (IAM) to control access to resources, how to implement encryption, and how to comply with industry standards and regulations.
- High availability and fault tolerance: The course covers how to design and implement applications on AWS that are highly available and fault tolerant. It explores techniques like AWS Auto-scaling. AWS Fault Tolerance, and AWS Load balancing cover best practices for ensuring that applications can survive and recover from failures.
At the end of the course, participants will be better prepared to take the AWS Certified DevOps Engineer Professional exam. They will have a deeper understanding of how to develop and deploy applications on AWS, and they will be familiar with the tools and services commonly used in DevOps workflows.
Step 4: Refer to AWS DevOps Engineer Professional Whitepapers
AWS offers a wide range of whitepapers that cover various topics related to AWS services and best practices. Reading these whitepapers is crucial as they provide in-depth knowledge about AWS services and their applications in real-world scenarios. The whitepapers cover topics such as cloud security, networking, DevOps, and more. You should focus on the whitepapers that are relevant to the exam objectives and go through them thoroughly to ensure you have a solid understanding of the concepts covered.
Step 5: Take AWS Certified DevOps Engineer Professional Practice Exam
Taking practice exams is a great way to check how well you know the exam topics and find out where you need to get better. The AWS Certified DevOps Engineer Professional Practice Exams are made to be like the real exam, so doing them will give you a good idea of what the real test will be like. It’s a good idea to take several practice exams and look at the answers to each question to understand why one choice is right and others are wrong. This will help you see what you don’t know well and work on improving it.
Step 6: Use AWS Services
The best way to learn AWS is to use it. AWS offers a range of services that can help you build, deploy, and manage applications on the cloud. Using these services will give you practical experience and help you understand how they work in real-world scenarios. You can start by creating an AWS account and exploring the services such as EC2, S3, Lambda, CloudFormation, and more. Additionally, you can create a test environment and practice deploying applications, setting up monitoring, and managing the infrastructure.
Step 7: Join the AWS Community
You can join online AWS study groups and forums to ask others who have previously taken the test or are studying for it about their concerns. You may even give each other tests to determine how prepared you are. Clearing your doubts can enhance your self-confidence and allow you to see into your weak areas. The conversation will aid in determining which portions require more preparation and which parts are adequately prepared.
Step 8: Learn from Books
AWS Certified Devops Engineer Professional Books are a reliable learning resource. For the security speciality test, there are a variety of publications available, which you may obtain online or in libraries. The following are some of the books that might help you arm yourself:
- AWS Automation Cookbook by Nikit Swaraj
- Continuous Delivery and DevOps – Quickstart by Paul Swartout
- Implementing DevOps on AWS by Veselin kantsev
- Effective DevOps with AWS by Nathenial Felson
Step 9: Evaluate your preparation with Practice Tests
Practice exams are the fundamental and crucial part of preparing for your exam. So, right now, all you need are the AWS Certified DevOps Engineer Professional practice tests. Solve as many sample papers and test sets as you can to figure out where you stand currently and how much more you need to prepare. There are many practice exams available online, so be sure to pick a reliable and trustworthy one to complete your preparation. These practice tests help you evaluate yourself and build confidence, and they also let you see how well you’re doing. Let’s Start Practising Now!
Expert’s Corner
The AWS Certified DevOps Engineer Professional certification is a challenging exam that requires a solid understanding of AWS services and their applications in real-world scenarios. To prepare for the exam, you should follow the tips we have provided, use the resources we have listed, and gain practical experience by using AWS services. Passing the exam will demonstrate your expertise in DevOps and open up new career opportunities.
