Published | 2021-08-05 |
Platform | Udemy |
Rating | 3.93 |
Number of Reviews | 26 |
Number of Students | 152 |
Price | $39.99 |
Instructors |
Rohit Singh
|
Subjects |
190+ Unique simulator Questions based on Professional Cloud DevOps Engineer (GCP) Certification Q&A to review your Prep
Professional Cloud DevOps Engineer (GCP) Certification Q&A
190+ Unique simulator Questions based on Professional Cloud DevOps Engineer (GCP) Certification Q&A to review your Preparations.
This program provides the skills you need to advance your career as a data engineer and provides training to support your preparation for the industry-recognized Google Cloud Professional DevOps Engineer certification. 87% of Google Cloud certified users feel more confident in their cloud skills.
You'll also have the opportunity to practice key job skills using Google Cloud to build software delivery pipelines, deploy and monitor services, and manage and learn from incidents. You will learn to apply SRE principles to a service, techniques for monitoring, troubleshooting, and improving infrastructure and application performance among other things.
About this certification exam
Length: Two hours
Registration fee: $200 (plus tax where applicable)
Language: English
Exam format: Multiple choice and multiple select
Certification exam guide
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 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
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 (e.g., staging, production, etc.):
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
Binary Authorization
IAM policies per environment
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
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
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:
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 (e.g., Cloud Build, Jenkins):
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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