Ultimate Google Certified Professional Cloud Developer 2025

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

2.1 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

3.1 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

Please go through Lab attached here.

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

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 CloudSQL 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

• Making API calls with a Cloud Client Library, the REST API, or the APIs Explorer, taking into consideration:

  • batching requests

  • restricting return data

  • paginating results

  • caching results

• Using service accounts to make Google API calls

• Using APIs to read/write to data services (BigQuery, Cloud Spanner)

• Using the Cloud SDK to perform basic tasks

5.2 Managing VMs. Tasks include:

• Debugging a custom VM image using the serial port

• Analyzing a failed Compute Engine VM startup

• Sending logs from a VM to Stackdriver

5.3 Viewing application performance metrics using Stackdriver. Tasks include:

• Creating a monitoring dashboard

• Viewing syslogs from a VM

• Writing custom metrics and creating metrics from logs

• Graphing metrics

• Using Stackdriver Debugger

• Streaming logs from the GCP Console

• Reviewing stack traces for error analysis

• Setting up log sinks

• Viewing logs in the GCP Console

• Profiling performance of request-response

• Profiling services

• Reviewing application performance using Stackdriver Trace and Stackdriver Logging

• Monitoring and profiling a running application

5.4 Diagnosing and resolving application performance issues. Tasks include:

• Setting up time checks and other basic alerts

• Setting up logging and tracing

• Setting up resources monitoring

• Troubleshooting network issues

• Debugging/tracing cloud apps

• Troubleshooting issues with the image/OS

• Using documentation, forums and Google support