​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 DEVELOPER 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 Developer Engineer sarai in grado di prendere decisioni basate sui dati, raccogliendo, trasformando e pubblicando i dati. Potrai progettare, sviluppare, rendere operativi, proteggere e monitorare i sistemi di elaborazione dei dati, con particolare attenzione alla sicurezza e alla conformità, alla scalabilità e all'efficienza, all'affidabilità e alla fedeltà, alla flessibilità e alla portabilità.

 

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 DEVELOPER ENGINEER

Professional Cloud Developer - Google Cloud Advanced Skills & Certification Workshop

Gestting started with Google Cloud Kubernetes Engine

Google Cloud Fundamentals: Core Infrastructure

Developing Applications with Google Cloud

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GOOGLE
PROFESSIONAL
PROFESSIONAL CLOUD DEVELOPER
CLOUD, IOT, artificialintelligence, machinelearning, bigdata, deeplearning, bigquery, developers

NewCondition 1600.00
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DURATION
3 days

COURSE DESCRIPTION
In this course, application developers learn how to design, develop, and deploy applications that seamlessly integrate components from the Google Cloud ecosystem. Through a combination of presentations, demos, and hands-on labs, participants learn how to use GCP services and pre-trained machine learning APIs to build secure, scalable, and intelligent cloud-native applications.

OBJECTIVES
This course teaches participants the following skills:

Use best practices for application development
Choose the appropriate data storage option for application data
Implement federated identity management
Develop loosely coupled application components or microservices
Integrate application components and data sources
Debug, trace, and monitor applications
Perform repeatable deployments with containers and deployment services
Choose the appropriate application runtime environment; use Google Container Engine as a runtime environment and later switch to a no-ops solution with Google App Engine Flex

AUDIENCE
Application developers who want to build cloud-native applications or redesign existing applications that will run on Google Cloud Platform.

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

Completed Google Cloud Platform Fundamentals or have equivalent experience
Working ​knowledge ​of Node.js
Basic proficiency with command-line tools and Linux operating system environments

TOPICS
Module 1: Best ​Practices ​for Application ​Development
Code and environment management
Design ​and ​development ​of ​secure, ​scalable, ​reliable, ​loosely ​coupled application ​components ​and ​microservices
Continuous ​integration ​and ​delivery
Re-architecting ​applications ​for ​the ​cloud
Module 2: Google ​Cloud ​Client Libraries, ​Google ​Cloud ​SDK, ​and Google ​Firebase ​SDK
How ​to ​set ​up ​and ​use ​Google ​Cloud ​Client ​Libraries, ​Google ​Cloud SDK, ​and ​Google ​Firebase ​SDK
Lab: ​Set ​up ​Google ​Client ​Libraries, ​Google ​Cloud ​SDK, ​and ​Firebase SDK ​on ​a ​Linux ​instance ​and ​set ​up ​application ​credentials
Module 3: Overview ​of ​Data Storage ​Options
Overview ​of ​options ​to ​store ​application ​data
Use ​cases ​for ​Google ​Cloud ​Storage, ​Google ​Cloud ​Datastore, ​Cloud Bigtable, ​Google ​Cloud ​SQL, ​and ​Cloud ​Spanner
Module 4: Best ​Practices ​for ​Using Cloud ​Datastore
Best ​practices ​related ​to ​the ​following:
Queries
Built-in ​and ​composite ​indexes
Inserting ​and ​deleting ​data ​(batch ​operations)
Transactions
Error ​handling
Bulk-loading ​data ​into ​Cloud ​Datastore ​by ​using ​Google ​Cloud Dataflow
Lab: ​Store ​application ​data ​in ​Cloud ​Datastore
Module 5: Performing ​Operations on ​Buckets ​and ​Objects
Operations ​that ​can ​be ​performed ​on ​buckets ​and ​objects
Consistency ​model
Error ​handling
Module 6: Best ​Practices ​for ​Using Cloud ​Storage
Naming ​buckets ​for ​static ​websites ​and ​other ​uses
Naming ​objects ​(from ​an ​access ​distribution ​perspective)
Performance ​considerations
Setting ​up ​and ​debugging ​a ​CORS ​configuration ​on ​a ​bucket
Lab: ​Store ​files ​in ​Cloud ​Storage
Module 7: Handling Authentication and Authorization
Cloud ​Identity ​and ​Access ​Management ​(IAM) ​roles ​and ​service accounts
User ​authentication ​by ​using ​Firebase ​Authentication
User ​authentication ​and ​authorization ​by ​using ​Cloud ​Identity-Aware Proxy
Lab: ​Authenticate ​users ​by ​using ​Firebase ​Authentication
Module 8: Using ​Google ​Cloud Pub/Sub ​to ​Integrate ​Components of ​Your ​Application
Topics, ​publishers, ​and ​subscribers
Pull ​and ​push ​subscriptions
Use ​cases ​for ​Cloud ​Pub/Sub
Lab: ​Develop ​a ​backend ​service ​to ​process ​messages ​in ​a ​message queue
Module 9: Adding ​Intelligence ​to Your ​Application
Overview ​of ​pre-trained ​machine ​learning ​APIs ​such ​as ​Cloud ​Vision API ​and ​Cloud ​Natural ​Language ​Processing ​API
Module 10: Using ​Cloud ​Functions for ​Event-Driven ​Processing
Key ​concepts ​such ​as ​triggers, ​background ​functions, ​HTTP ​functions
Use ​cases
Developing ​and ​deploying ​functions
Logging, ​error ​reporting, ​and ​monitoring
Module 11: ​Managing APIs with Google Cloud Endpoints
Open ​API ​deployment ​configuration
Lab: ​Deploy ​an ​API ​for ​your ​application
Module 12: Deploying ​an Application ​by ​Using ​Google ​Cloud ​Build, ​Google ​Cloud Container ​Registry, ​and ​Google Cloud ​Deployment ​Manager
Creating ​and ​storing ​container ​images
Repeatable ​deployments ​with ​deployment ​configuration ​and templates
Lab: ​Use ​Deployment ​Manager ​to ​deploy ​a ​web ​application ​into Google ​App ​Engine ​flexible environment test ​and ​production ​environments
Module 13: Execution Environments ​for ​Your ​Application
Considerations ​for ​choosing ​an ​execution ​environment ​for ​your application ​or ​service:
Google ​Compute ​Engine
Kubernetes ​Engine
App ​Engine ​flexible environment
Cloud ​Functions
Cloud ​Dataflow
Lab: ​Deploying ​your ​application ​on ​App ​Engine flexible environment
Module 14: Debugging, Monitoring, and Tuning Performance by Using ​Google Stackdriver
Stackdriver ​Debugger
Stackdriver ​Error ​Reporting
Lab: ​Debugging ​an ​application ​error ​by ​using ​Stackdriver ​Debugger and ​Error ​Reporting
Stackdriver Logging
Key concepts related to Stackdriver Trace and Stackdriver Monitoring.
Lab: Use Stackdriver Monitoring and Stackdriver Trace to trace a request across services, observe, and optimize performance

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