​K Labs mette a tua disposizione i propri Trainers Certificati, i Laboratori Didattici, i Simulatori di Esame, il proprio Test Center e un Tutor a te dedicato per la preparazione all'esame.


Grazie al nostro supporto la percentuale di candidati che ottengono la certificazione al primo tentativo è prossima al 100%.​

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PROFESSIONAL DEVOPS ENGINEER

K Labs

Le certificazioni Google Cloud Professional valutano le competenze avanzate in progettazione, implementazione e management.

Queste certificazioni sono consigliate a persone con esperienza nel settore e che hanno dimistichezza nell'utilizzo dei prodotti e delle soluzioni Google Cloud.

 

Con la certificazione Google Cloud Professional DevOps Engineer sarai responsabile di operazioni di sviluppo efficienti in grado di bilanciare l'affidabilità del servizio e la velocità di delivery. Utilizzerai la Google Cloud Platform per costruire pipeline di distribuzione del software, implementare e monitorare i servizi, gestire e imparare dagli eventi.

 

Propedeuticità dei corsi:

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K Labs S.r.l.

Tel. +39 059 8212 29 | info@klabs.it

P.Iva IT02034520367

GOOGLE CLOUD - PROFESSIONAL CLOUD DEVOPS ENGINEER

Architecting with Google Kubernetes Engine

Architecting with Google Cloud: Design and Process

Logging, monitoring and observability in Google Cloud

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GOOGLE
PROFESSIONAL
PROFESSIONAL DEVOPS ENGINEER
CLOUD, IOT, Kubernetes, google kubernetes engine, machinelearning, bigdata, deeplearning, bigquery, developers, virtualcontainers, app, iaas, paas, saas, serverless, devops, irtualmachines

NewCondition 1600.00
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DURATION
3 days

COURSE DESCRIPTION
This three-day instructor-led class introduces participants to deploying and managing containerized applications on Google Kubernetes Engine (GKE) and the other services provided by Google Cloud Platform. Through a combination of presentations, demos, and hands-on labs, participants explore and deploy solution elements, including infrastructure components such as pods, containers, deployments, and services; as well as networks and application services. This course also covers deploying practical solutions including security and access management, resource management, and resource monitoring.

OBJECTIVES
This course teaches participants the following skills:

Understand how software containers work
Understand the architecture of Kubernetes
Understand the architecture of Google Cloud Platform
Understand how pod networking works in Kubernetes Engine
Create and manage Kubernetes Engine clusters using the GCP Console and gcloud/kubectl commands
Launch, roll back and expose jobs in Kubernetes
Manage access control using Kubernetes RBAC and Google Cloud IAM
Managing pod security policies and network policies
Using Secrets and ConfigMaps to isolate security credentials and configuration artifacts
Understand GCP choices for managed storage services
Monitor applications running in Kubernetes Engine

AUDIENCE
This class is intended for the following participants:

Cloud architects, administrators, and SysOps/DevOps personnel
Individuals using Google Cloud Platform to create new solutions or to integrate existing systems, application environments, and infrastructure with the Google Cloud Platform.

PREREQUISITES
To get the most out of this course, participants should have:

Completed Google Cloud Platform Fundamentals: Core Infrastructure or have equivalent
experience
Basic proficiency with command-line tools and Linux operating system environments

TOPICS
Module 1: Introduction to Google Cloud Platform
Use the Google Cloud Platform Console
Use Cloud Shell
Define cloud computing
Identify GCP’s compute services
Understand regions and zones
Understand the cloud resource hierarchy
Administer your GCP resources
Module 2: Containers and Kubernetes in GCP
Create a container using Cloud Build
Store a container in Container Registry
Understand the relationship between Kubernetes and Google Kubernetes Engine (GKE)
Understand how to choose among GCP compute platforms
Module 3: Kubernetes Architecture
Understand the architecture of Kubernetes: pods, namespaces
Understand the control-plane components of Kubernetes
Create container images using Google Cloud Build
Store container images in Google Container Registry
Create a Kubernetes Engine cluster
Module 4: Kubernetes Operations
Work with the kubectl command
Inspect the cluster and Pods
View a Pod’s console output
Sign in to a Pod interactively
Module 5: Deployment, Jobs, and Scaling
Create and use Deployments
Create and run Jobs and CronJobs
Scale clusters manually and automatically
Configure Node and Pod affinity
Get software into your cluster with Helm charts and Kubernetes Marketplace
Module 6: GKE Networking
Create Services to expose applications that are running within Pods
Use load balancers to expose Services to external clients
Create Ingress resources for HTTP(S) load balancing
Leverage container-native load balancing to improve Pod load balancing
Define Kubernetes network policies to allow and block traffic to pods
Module 7: Persistent Data and Storage
Use Secrets to isolate security credentials
Use ConfigMaps to isolate configuration artifacts
Push out and roll back updates to Secrets and ConfigMaps
Configure Persistent Storage Volumes for Kubernetes Pods
Use StatefulSets to ensure that claims on persistent storage volumes persist across restarts
Module 8: Access Control and Security in Kubernetes and Kubernetes Engine
Understand Kubernetes authentication and authorization
Define Kubernetes RBAC roles and role bindings for accessing resources in namespaces
Define Kubernetes RBAC cluster roles and cluster role bindings for accessing cluster-scoped resources
Define Kubernetes pod security policies
Understand the structure of GCP IAM
Define IAM roles and policies for Kubernetes Engine cluster administration
Module 9: Logging and Monitoring
Use Stackdriver to monitor and manage availability and performance
Locate and inspect Kubernetes logs
Create probes for wellness checks on live applications
Module 10: Using GCP Managed Storage Services from Kubernetes Applications
Understand pros and cons for using a managed storage service versus self-managed containerized storage
Enable applications running in GKE to access GCP storage services
Understand use cases for Cloud Storage, Cloud SQL, Cloud Spanner, Cloud Bigtable, Cloud Firestore, and BigQuery from within a Kubernetes application

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