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Google Professional Cloud DevOps Engineer (GCP) 

  • Offered byEXIN

Google Professional Cloud DevOps Engineer (GCP)
 at 
EXIN 
Overview

Total fee

799

Mode of learning

Online

Schedule type

Self paced

Credential

Certificate

Google Professional Cloud DevOps Engineer (GCP)
 at 
EXIN 
Highlights

  • Full-Length Mock Test with unique questions in each test set
  • Practice objective questions with section-wise scores
  • In-depth and exhaustive explanation for every question
  • Reliable exam reports to evaluate strengths and weaknesses
  • Latest Questions with an updated version
Read more
Details Icon

Google Professional Cloud DevOps Engineer (GCP)
 at 
EXIN 
Course details

What are the course deliverables?
  • Apply site reliability engineering principles to a service
  • Optimize service performance
  • Implement service monitoring strategies
  • Build and implement CI/CD pipelines for a service
  • Manage service incidents
More about this course
  • GCP exam has been designed to test technical skills related to the job role
  • Candidates preparing for the exam should have hands-on experience
  • Candidates planning to go for Professional Cloud DevOps Engineer will be responsible for efficient development operations that can balance service reliability and delivery speed
  • Also, they should have the skills to use Google Cloud Platform to build software delivery pipelines, deploy and monitor services, and manage and learn from incidents

Google Professional Cloud DevOps Engineer (GCP)
 at 
EXIN 
Curriculum

Module 1 - Applying site reliability engineering principles to a service

1.1 Balance change, velocity, and reliability of the service -

Discover SLIs (availability, latency, etc.)

Define SLOs and understand SLAs

Agree to the consequences of not meeting the error budget

Construct feedback loops to decide what to build next

Toil automation

1.2 Manage service life cycle -

Manage a service (e.g., introduce a new service, deploy it, maintain and retire it)

Plan for capacity (e.g., quotas and limits management)

1.3 Ensure healthy communication and collaboration for operations:

Prevent burnout (e.g., set up automation processes to prevent burnout)

Foster a learning culture

Foster a culture of blamelessness

Module 2 - Building and implementing CI/CD pipelines for a service

2.1 Design CI/CD pipelines:

Immutable artifacts with Container Registry

Artifact repositories with Container Registry

Deployment strategies with Cloud Build, Spinnaker

Deployment to hybrid and multi-cloud environments with Anthos, Spinnaker, Kubernetes

Artifact versioning strategy with Cloud Build, Container Registry

CI/CD pipeline triggers with Cloud Source Repositories, Cloud Build GitHub App, Cloud Pub/Sub

Testing a new version with Spinnaker

Configure deployment processes (e.g., approval flows)

2.2 Implement CI/CD pipelines -

CI with Cloud Build

CD with Cloud Build

Open source tooling (e.g. Jenkins, Spinnaker, GitLab, Concourse)

Auditing and tracing of deployments (e.g., CSR, Cloud Build, Cloud Audit Logs)

2.3 Manage configuration and secrets -

Secure storage methods

Secret rotation and config changes

2.4 Manage infrastructure as code -

Terraform / Cloud Deployment Manager

Infrastructure code versioning

Make infrastructure changes safer

Immutable architecture

2.5 Deploy CI/CD tooling:

Centralized tools vs. multiple tools (single vs multi-tenant)

Security of CI/CD tooling

2.6 Manage different development environments -

Decide on the number of environments and their purpose

Create environments dynamically per feature branch with GKE, Cloud Deployment Manager

Local development environments with Docker, Cloud Code, Skaffold

2.7 Secure the deployment pipeline:

Vulnerability analysis with Container Registry, Cloud Security Scanner

Binary Authorization

IAM policies per environment

Module 3 - Implementing service monitoring strategies

3.1 Manage application logs -

Collecting logs from Compute Engine, GKE with Stackdriver Logging, Fluentd

Collecting third-party and structured logs with Stackdriver Logging, Fluentd

Sending application logs directly to Stackdriver API with Stackdriver Logging

3.2 Manage application metrics with Stackdriver Monitoring -

Collecting metrics from Compute Engine

Collecting GKE/Kubernetes metrics

Use metric explorer for ad hoc metric analysis

3.3 Manage Stackdriver Monitoring platform:

Creating a monitoring dashboard

Filtering and sharing dashboards

Configure third-party alerting in Stackdriver Monitoring (i.e., PagerDuty, Slack, etc.)

Define alerting policies based on SLIs with Stackdriver Monitoring

Automate alerting policy definition with Cloud DM or Terraform

Implementing SLO monitoring and alerting with Stackdriver Monitoring

Understand Stackdriver Monitoring integrations (e.g., Grafana, BigQuery)

Using SIEM tools to analyze audit/flow logs (e.g., Splunk, Datadog)

Design Stackdriver Workspace strategy

3.4 Manage Stackdriver Logging platform:

Enabling data access logs (e.g., Cloud Audit Logs)

Enabling VPC flow logs

Viewing logs in the GCP Console

Using basic vs. advanced logging filters

Implementing logs-based metrics

Understanding the logging exclusion vs. logging export

Selecting the options for logging export

Implementing a project-level / org-level export

Viewing export logs in Cloud Storage and BigQuery

Sending logs to an external logging platform

3.5 Implement logging and monitoring access controls -

Set ACL to restrict access to audit logs with IAM, Stackdriver Logging

Set ACL to restrict export configuration with IAM, Stackdriver Logging

Set ACL to allow metric writing for custom metrics with IAM, Stackdriver Monitoring

Module 4 - Optimizing service performance

4.1 Identify service performance issues -

Evaluate and understand user impact (Stackdriver Service Monitoring for App Engine, Istio)

Utilize Stackdriver to identify cloud resource utilization

Utilize Stackdriver Trace/Profiler to profile performance characteristics

Interpret service mesh telemetry

Troubleshoot issues with the image/OS

Troubleshoot network issues (e.g., VPC flow logs, firewall logs, latency, view network details)

4.2 Debug application code -

Application instrumentation

Stackdriver Debugger

Stackdriver Logging

Stackdriver Trace

Debugging distributed applications

App Engine local development server

Stackdriver Error Reporting

Stackdriver Profiler

4.3 Optimize resource utilization -

Identify resource costs

Identify resource utilization levels

Develop plan to optimize areas of greatest cost or lowest utilization

Manage preemptible VMs

Work with committed-use discounts

TCO considerations

Consider network pricing

Module 5 - Managing service incidents

5.1 Coordinate roles and implement communication channels during a service incident

Define roles (incident commander, communication lead, operations lead)

Handle requests for impact assessment

Provide regular status updates, internal and external

Record major changes in incident state (When mitigated? When all clear? etc.)

Establish communications channels (email, IRC, Hangouts, Slack, phone, etc.)

Scaling response team and delegation

Avoid exhaustion / burnout

Rotate / hand over roles

Manage stakeholder relationships

5.2 Investigate incident symptoms impacting users with Stackdriver IRM

Identify probable causes of service failure

Evaluate symptoms against probable causes; rank probability of cause based on observed behavior

Perform investigation to isolate most likely actual cause

Identify alternatives to mitigate issue

5.3 Mitigate incident impact on users

Roll back release

Drain / redirect traffic

Turn off experiment

Add capacity

5.4 Resolve issues

Code change / fix bug

Verify fix

Declare all-clear

5.5 Document issue in a postmortem

Document root causes

Create and prioritize action items

Communicate postmortem to stakeholders

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Google Professional Cloud DevOps Engineer (GCP)
 at 
EXIN 

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