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Google Cloud Professional Cloud Developer Practice Exam

Course

GOOGLE CLOUD PROFESSIONAL CLOUD DEVELOPER PRACTICE EXAM

Category

Google Cloud Professional Cloud DeveloperIT Training

Eligibility

Job Aspirants

Mode

Both Classroom and Online Classes

Batches

Week Days and Week Ends

Duration :

45 Days

Google Cloud Professional Cloud Developer Objectives

•Learn how to work with Google Cloud Professional Cloud Developer .
•Explore different tools used for the Google Cloud Professional Cloud Developer .
•Learn Google Cloud Professional Cloud Developer Programming The Fast and Easy Way!
•How to write Google Cloud Professional Cloud Developer scripts to automate redundant tasks.
•You will know how to design Google Cloud Professional Cloud Developer from scratch.
•How to build your own apps and scripts using Google Cloud Professional Cloud Developer .
•Learn all the hooks and crooks of Google Cloud Professional Cloud Developer at your pace.
•An easy way to learn Google Cloud Professional Cloud Developer and start coding right away!
•Gain the ability to adapt to any coding language with the concepts of Google Cloud Professional Cloud Developer

Google Cloud Professional Cloud Developer Practice Exam Course Features

•Career guidance providing by It Expert
•Free technical support for students
•Learn Core concepts from Leading Instructors
•Classes are Accessible on Website and Mobile Apps
•Facility of Lab on cloud available (based on booking)
•Collaboration With 500+ Clients for Placements and Knowledge Sessions
•Make aware of code competence in building extensive range of applications using Python
•Very in depth course material with Real Time Scenarios for each topic with its Solutions for Online Trainings.

Who are eligible for Google Cloud Professional Cloud Developer

•.Net Developer, PL SQL developer, UI Designer, Data Analyst, Business Analyst
•Java Developer, Front End Developer, Visionplus Developer, Automation Testing, Selenium/ Tosca Testing, Functional Testing, Mainframe Developer, Connex
•Magento, Java, Adfs, Mule Esb, Dell Boomi, Backend Developer, Sap Bo, Sap Apo, .Net, L2 Deveoper, Python Developer, Quality Assurance Engineering, Etl
•Qa Testers / Developers, Full Stack Developers – Backend / Frontend, Power Bi, Market Intelligence
•Storage Domain Professionals, SAN, NAS, Devops Engineer, Developer – Storage Domain, File System, Storage Domain, RAID, CIFS, NFS, Linux, Kernel Programming

GOOGLE CLOUD PROFESSIONAL CLOUD DEVELOPER PRACTICE EXAM

A Professional Cloud Developer builds scalable and highly available applications using Google recommended practices and tools that leverage fully managed services. This individual has experience with next generation databases, runtime environments and developer tools. They also have proficiency with at least one general purpose programming language and are skilled with using Stackdriver to produce meaningful metrics and logs to debug and trace code Sample case study Some of the questions on the Professional Cloud Developer certification exam may refer you to a case study that describes a fictitious business and solution concept. This case study is intended to provide additional context to help you choose your answer(s). We recommend that you review the sample case study that may be used in the exam. HipLocal A Professional Google Cloud Developer builds scalable and highly available applications using Google recommended practices and tools that leverage fully managed services. They have hands on experience with next generation databases, runtime environments and developer tools. This individual has proficiency with at least one general purpose programming language to define new APIs and invoke Google APIs and are skilled with using Stackdriver to produce meaningful metrics and logs to debug and trace code. *Note: The exam does not directly assess coding skill. It focuses on your ability to leverage GCP services and recommended practices in order to build, test, deploy, and manage scalable and highly available applications. If you are proficient in at least one general purpose coding language, you should be able to interpret any questions that present code snippets. 1. Designing highly scalable, available, and reliable cloud-native applications 1.1 Designing performant applications and APIs. Considerations include: Infrastructure as a Service vs. Container as a Service vs. Platform as a Service (e.g., autoscaling implications) Portability vs. platform-specific design Evaluating different services and technologies Operating system versions and base runtimes of services Geographic distribution of Google Cloud services Microservices Defining a key structure for high write applications using Cloud Storage, Cloud Bigtable, Cloud Spanner, or Cloud SQL Session management Deploying and securing an API with cloud endpoints Loosely coupled applications using asynchronous Cloud Pub/Sub events Health checks Google-recommended practices and documentation 1.2 Designing secure applications. Considerations include: Applicable regulatory requirements and legislation Security mechanisms that protect services and resources Storing and rotating secrets IAM roles for users/groups/service accounts HTTPs certificates Google-recommended practices and documentation 1.3 Managing application data. Tasks include: Defining database schemas for Google-managed databases (e.g., Cloud Datastore, Cloud Spanner, Cloud Bigtable, BigQuery) Choosing data storage options based on use case considerations, such as: Cloud Storage signed URLs for user-uploaded content Using Cloud Storage to run a static website Structured vs. unstructured data ACID transactions vs. analytics processing Data volume Frequency of data access in Cloud Storage Working with data ingestion systems (e.g., Cloud Pub/Sub, Storage Transfer Service) Following Google-recommended practices and documentation 1.4 Re-architecting applications from local services to Google Cloud Platform. Tasks include: Using managed services Using the strangler pattern for migration Google-recommended practices and documentation 2. Building and Testing Applications 2.1 Setting up your development environment. Considerations include: Emulating GCP services for local application development Creating GCP projects 2.2 Building a continuous integration pipeline. Considerations include: Creating a Cloud Source Repository and committing code to it Creating container images from code Developing unit tests for all code written Developing an integration pipeline using services (e.g., Cloud Build, Container Registry) to deploy the application to the target environment (e.g., development, test, staging) Reviewing test results of continuous integration pipeline 2.3 Testing. Considerations include: Performance testing Integration testing Load testing 2.4 Writing code. Considerations include: Algorithm design Modern application patterns Efficiency Agile methodology 3. Deploying applications 3.1 Implementing appropriate deployment strategies based on the target compute environment (Compute Engine, Google Kubernetes Engine, App Engine). Strategies include: Blue/green deployments Traffic-splitting deployments Rolling deployments Canary deployments 3.2 Deploying applications and services on Compute Engine. Tasks include: Launching a compute instance using GCP Console and Cloud SDK (gcloud) (e.g., assign disks, availability policy, SSH keys) Moving a persistent disk to different VM Creating an autoscaled managed instance group using an instance template Generating/uploading a custom SSH key for instances Configuring a VM for Stackdriver monitoring and logging Creating an instance with a startup script that installs software Creating custom metadata tags Creating a load balancer for Compute Engine instances 3.3 Deploying applications and services on Google Kubernetes Engine. Tasks include: Deploying a GKE cluster Deploying a containerized application to GKE Configuring GKE application monitoring and logging Creating a load balancer for GKE instances Building a container image using Cloud Build 3.4 Deploying an application to App Engine. Considerations include: Scaling configuration Versions Traffic splitting Blue/green deployment 3.5 Deploying a Cloud Function. Types include: Cloud Functions that are triggered via an event (e.g., Cloud Pub/Sub events, Cloud Storage object change notification events) Cloud Functions that are invoked via HTTP 3.6 Creating data storage resources. Tasks include: Creating a Cloud Repository Creating a Cloud SQL instance Creating composite indexes in Cloud Datastore Creating BigQuery datasets Planning and deploying Cloud Spanner Creating a Cloud Storage bucket Creating a Cloud Storage bucket and selecting appropriate storage class Creating a Cloud Pub/Sub topic 3.7 Deploying and implementing networking resources. Tasks include: Creating an auto mode VPC with subnets Creating ingress and egress firewall rules for a VPC (e.g., IP subnets, Tags, Service accounts) Setting up a domain using Cloud DNS 3.8 Automating resource provisioning with Deployment Manager 3.9 Managing Service accounts. Tasks include: Creating a service account with a minimum number of scopes required Downloading and using a service account private key file 4. Integrating Google Cloud Platform Services 4.1 Integrating an application with Data and Storage services. Tasks include: Enabling BigQuery and setting permissions on a dataset Writing an SQL query to retrieve data from relational databases Analyzing data using BigQuery Fetching data from various databases Enabling Cloud SQL and configuring an instance Connecting to a Cloud SQL instance Enabling Cloud Spanner and configuring an instance Creating an application that uses Cloud Spanner Configuring a Cloud Pub/Sub push subscription to call an endpoint Connecting to and running a Cloud SQL query Storing and retrieving objects from Google Storage Publishing and consuming from Data Ingestion sources Reading and updating an entity in a Cloud Datastore transaction from an application Using the CLI tools Provisioning and configuring networks 4.2 Integrating an application with compute services. Tasks include: Implementing service discovery in Google Kubernetes Engine, App Engine, and Compute Engine Writing an application that publishes/consumes from Cloud Pub/Sub Reading instance metadata to obtain application configuration Authenticating users by using Oauth2 Web Flow and Identity Aware Proxy Using the CLI tools Configuring compute services network settings (e.g., subnet, firewall ingress/egress, public/private IPs) 4.3 Integrating Google Cloud APIs with applications. Tasks include: Enabling a GCP API Using pre-trained Google ML APIs Makin