Professional Cloud DevOps Engineer Certification

Professional Cloud DevOps Engineer Exam Prep Workshop

DreamsPlus offers an extensive Professional Cloud DevOps Engineer exam preparation workshop in Singapore and online, designed to provide cloud engineers with hands-on experience and prepare them for the Google Professional Cloud DevOps Engineer certification. Led by expert trainers, this workshop ensures you acquire the practical knowledge and skills essential to excel in the exam. Whether you’re aiming for Cloud DevOps Engineer certification, seeking Cloud DevOps training in Singapore, or aspiring to become a Professional Cloud DevOps Engineer, this workshop delivers the critical expertise needed for your success.

Workshop Overview:

– 2-day intensive exam prep workshop

– Expert trainers with real-world experience

– Comprehensive course material

– Interactive sessions and group discussions

– Practice exams and assessments

Training Curriculum:

Section 1: Bootstrapping a Google Cloud organization for DevOps

1.1 Designing the overall resource hierarchy for an organization.       

• Folders and projects 

• Network sharing 

• Roles and organization-level policies for Identity and Access Management (IAM) 

• Service account creation and management 

  1.2 Managing infrastructure as code. 

• Utilizing Cloud Foundation Toolkit, Config Connector, Terraform, Helm, and other infrastructure as code tools
• Making changes to infrastructure with Google-recommended procedures and infrastructure as code

•   Static architecture 

  1.3 Designing a CI/CD architecture stack in Google Cloud, hybrid, and multi-cloud environments. 

• CI with Cloud Build
• CD with Google Cloud Deploy
• Widely used third-party tooling (e.g., Jenkins, Git, ArgoCD, Packer)
• Security of CI/CD tooling

  1.4 Managingmultiple environments (e.g., staging, production). 

• Calculating the quantity and use of surroundings
• Using Terraform and Google Kubernetes Engine (GKE) to create environments dynamically           for each feature branch 
• Configuration Management 

Section 2: Building and implementing CI/CD pipelines for a service 

  2.1 Designing and managing CI/CD pipelines. 

• CI/CD pipeline triggers
• deployment to hybrid and multi-cloud environments (e.g., Anthos, GKE)
• artifact management with Artifact Registry
• Testing an upcoming version of an application
• Setting up deployment procedures (approval flows, for example)
• Serverless application CI/CD

   2.2Implementing CI/CD pipelines. 

• Tracking and auditing deployments (such as via the Cloud Build, Google Cloud Deploy, Artifact Registry, and Cloud Audit Logs).
• Implementation techniques (such as rolling, blue/green, traffic dividing, canary, etc.)
• Reverse tactics
• Resolving problems with deployment 

   2.3Managing CI/CD configuration and secrets. 

• Techniques for safe storage and services for key rotation (like Secret Manager and Cloud Key Management)
• Covert operations
• Construct versus runtime covert insertion 

   2.4Securing the CI/CD deployment pipeline. 

• Vulnerability assessment using the Artifact Registry
• Binary Authorization
• Environment-specific IAM rules 

Section 3: Applying site reliability engineering practices to a service 

   3.1 Balancing change, velocity, and reliability of the service. 

• Finding SLIs (such as latency, availability) 
• Defining SLOs and comprehending SLAs
• Error budgets 
• Toil automation 
• Opportunity cost of risk and reliability (e.g., numerical value of “nines”)

3.2 Managing service lifecycle. 

• Service management, which includes introducing new services through the use of launch plans, deployment plans, maintenance schedules, and retirement plans.
• Capacity planning, such as managing quotas and restrictions
• Autoscaling with GKE, Cloud Run, Cloud Functions, or managed instance groups
• Putting feedback loops in place to make services better 

   3.3 Ensuring healthy communication and collaboration for operations. 

• Preventing burnout (e.g., setting up automation processes to prevent burnout)
• Fostering a culture of learning and blamelessness
• Establishing joint ownership of services to eliminate team silos

   3.4 Mitigating incident impact on users.

• Communicating during an incident
• Draining/redirecting traffic
• Adding capacity

   3.5 Conducting a postmortem. 

• Documenting root causes
• Creating and prioritizing action items
• Communicating the postmortem to stakeholders

Section 4: Implementing service monitoring strategies 

    4.1Managing logs. 

•  Using Cloud Logging to gather both structured and unstructured logs from serverless platforms, Compute Engine, and GKE
• Configuring the Cloud Logging agent
• Gathering logs from sources other than Google Cloud
• Directly sending application logs to the Cloud Logging API
• Log levels (e.g., info, error, debug, fatal)
• Optimizing logs (e.g., multiline logging, exceptions, size, cost) 

   4.2 Managing metrics with Cloud Monitoring. 

• Gathering and evaluating data from applications and platforms
• Getting data via networking and service mesh 
• Ad hoc metric analysis using Metrics Explorer 
• Building custom metrics from logs 

 4.3 Managing dashboards and alerts in Cloud Monitoring. 

• Building a monitoring dashboard 
• Filtering and sharing dashboards
• Setting up alerts
• Defining alerting policies based on SLOs and SLIs
• Automating the creation of alerting policies using Terraform
• Collecting metrics and setting up monitoring and alerting using Google Cloud Managed Service for Prometheus 

   4.4 Managing Cloud Logging platform. 

• Turning on data access logs (like Cloud Audit Logs)
• Turning on VPC Flow Logs 
• Seeing logs in the Google Cloud UI 
• Using primary versus secondary log filters 
• Logs exclusion versus logs export
• Exporting at the project versus organization level
• Controlling and observing log exports
• Forwarding logs to an external logging platform 
• Filtering and removing sensitive information (such as protected health information [PHI] and personally identifiable information [PII]) 

   4.5 Implementing logging and monitoring access controls. 

• Restricting access to audit logs and VPC Flow Logs with Cloud Logging
• Restricting export configuration with Cloud Logging
• Allowing metric and log writing with Cloud Monitoring

Section 5: Optimizing the service performance

   5.1 Identifying service performance issues. 

• Utilizing Google Cloud’s operations suite to determine how cloud resources are being used Analyzing service mesh telemetry 
• Debugging compute resource problems
• Debugging application deploy and runtime problems 
• Debugging network problems (e.g., VPC Flow Logs, firewall logs, latency, network details) 

   5.2 Implementing debugging tools in Google Cloud. 

• Application instrumentation
• cloud logging
• cloud trace
• cloud logging
• error reporting
• cloud profiling
• cloud monitoring
• cloud trace 

   5.3 Optimizing resource utilization and costs. 

• Committed-use discounts (e.g., flexible, resource-based)
• Sustained-use discounts
• Preemptible/Spot virtual machines (VMs)
• Levels of the network
• Sizing suggestions