This Cloud Developer practice exam will familiarize you with the types of questions you may encounter on the certification exam and help you determine your readiness or if you need more preparation and/or experience. 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. The Professional Cloud Developer exam assesses your ability to: Design highly scalable, available, and reliable cloud-native applications Build and test applications Deploy applications Integrate Google Cloud Platform services Manage application performance monitoring Google Cloud’s certification program gives Google Cloud users, customers and partners a way to demonstrate their technical skills in a particular job role or technology. Individuals are assessed using a variety of rigorously developed industry standard methods to determine whether they meet Google Cloud’s proficiency standards. Unless explicitly stated in the detailed exam descriptions, all Google Cloud certifications are valid for two years from the date certified. Candidates must recertify in order to maintain their certification status. How can I prepare for the exams? Real-world, hands-on experience is the best preparation. Google certification exams are designed to identify individuals who demonstrate skills using Google technology to perform critical job tasks. Google list these tasks in our exam guides. Please refer to the individual exam pages to review the relevant exam guide and learning resources that we believe can help you build the foundation you need to get started 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