Course Summary

Professional Cloud Developer
A Professional Cloud Developer builds and deploys scalable, secure, and highly available applications by using Google-recommended tools and best practices. This individual has experience with cloud-native applications, containerized applications, APIs, developer tools, orchestration tools, managed services, test strategies, serverless platforms, and next-generation databases. This individual also has proficiency with at least one general-purpose programming language and instruments their code to produce metrics, logs, and traces.

The Professional Cloud Developer exam assesses your ability to:

Design highly scalable, available, reliable cloud-native applications
Build and test applications
Deploy applications
Integrate an application with Google Cloud services

Section 1: Designing highly scalable, available, and reliable cloud-native applications

1.1 Designing high-performing applications and APIs. Considerations include:

● Microservices architecture

● Choosing the appropriate platform based on the use case and requirements (e.g., IaaS [infrastructure as a service], CaaS [container as a service], PaaS [platform as a service], FaaS [function as a service])

● Application modernization (e.g., containerization)

● Understanding how Google Cloud services are geographically distributed (e.g., latency, regional services, zonal services)

● User session management

● Caching solutions

● HTTP REST versus gRPC (Google Remote Procedure Call)

● Incorporating Service Control capabilities offered by API services (e.g. Apigee)

● Loosely coupled asynchronous applications (e.g., Apache Kafka, Pub/Sub, Eventarc)

● Instrumenting code to produce metrics, logs, and traces

● Cost optimization and resource optimization

● Graceful handling of errors, disasters, and scaling events

1.2 Designing secure applications. Considerations include:

● Implementing data lifecycle and residency for applicable regulatory requirements

● Security mechanisms that identify vulnerabilities and protect services and resources (e.g., Identity-Aware Proxy [IAP], Web Security Scanner)

● Security mechanisms that secure/scan application binaries, dependencies, and manifests (e.g., Container Analysis)

● Storing, accessing, and rotating application secrets and encryption keys (e.g., Secret Manager, Cloud Key Management Service)

● Authenticating to Google Cloud services (e.g., application default credentials, JSON Web Token [JWT], OAuth 2.0)

● End-user account management and authentication by using Identity Platform

● Identity and Access Management (IAM) roles for users, groups, and service accounts

● Securing service-to-service communications (e.g., service mesh, Kubernetes Network Policies, Kubernetes namespaces)

● Running services with keyless and least privileged access (e.g., Workload Identity, Workload identity federation)

● Certificate-based authentication (e.g., SSL, mTLS)

● Supply-chain Levels for Software Artifacts (SLSA)

1.3 Choosing storage options for application data. Considerations include:

● Time-limited access to objects

● Data retention requirements

● Structured versus unstructured data (e.g., SQL versus NoSQL)

● Strong versus eventual consistency

● Data volume

● Data access patterns

● Online transaction processing (OLTP) versus data warehousing

Section 2: Building and testing applications

2.1 Setting up your local development environment. Considerations include:

● Emulating Google Cloud services for local application development

● Using the Google Cloud console, Google Cloud SDK, Cloud Shell, and Cloud Workstations

● Using developer tooling (e.g., common IDEs, Cloud Code, Skaffold)

● Authenticating to Google Cloud services (e.g., Cloud SQL Auth proxy, AlloyDB Auth proxy)

2.2 Building. Considerations include:

● Source control management

● Creating secure container images from code

● Developing a continuous integration pipeline by using services (e.g., Cloud Build, Artifact Registry) that construct deployment artifacts

● Code and test build optimization

2.3 Testing. Considerations include:

● Unit testing

● Integration testing including the use of emulators

● Performance testing

● Load testing

● Failure testing/chaos engineering

Section 3: Deploying applications

3.1 Adopting appropriate feature rollout strategies. Considerations include:

● A/B testing

● Feature flags

● Backward compatibility

● Versioning APIs (e.g., Apigee)

3.2 Deploying applications to a serverless computing environment. Considerations include:

● Deploying applications from source code

● Using triggers to invoke functions

● Configuring event receivers (e.g., Eventarc, Pub/Sub)

● Exposing and securing application APIs (e.g., Apigee)

3.3 Deploying applications and services to Google Kubernetes Engine (GKE). Considerations include:

● Deploying a containerized application to GKE

● Integrating Kubernetes role-based access control (RBAC) with IAM

● Defining workload specifications (e.g., resource requirements)

● Building a container image by using Cloud Build

Section 4: Integrating an application with Google Cloud services

4.1 Integrating an application with data and storage services. Considerations include:

● Managing connections to datastores (e.g., Cloud SQL, Firestore, Bigtable, Cloud Storage)

● Reading/writing data to or from various datastores

● Writing an application that publishes or consumes data asynchronously (e.g., from Pub/Sub or streaming data sources)

● Orchestrate application services with Workflows, Eventarc, Cloud Tasks, and Cloud Scheduler

4.2 Integrating an application with Google Cloud APIs. Considerations include:

● Enabling Google Cloud services

● Making API calls by using supported options (e.g., Cloud Client Library, REST API or gRPC, API Explorer) taking into consideration:

○ Batching requests

○ Restricting return data

○ Paginating results

○ Caching results

○ Error handling (e.g., exponential backoff)

● Using service accounts to make Cloud API calls

● Integrating with Google Cloud’s operations suite

Prerequisites: None Recommended experience: 3+ years of industry experience including 1+ years designing and managing solutions using Google Cloud.

About the current GA certification exam Length: Two hours Registration fee: $200 (plus tax where applicable) Languages: English and Japanese Exam format: 50-60 multiple choice and multiple select questions Certification Renewal / Recertification: Candidates must recertify in order to maintain their certification status. Unless explicitly stated in the detailed exam descriptions, all Google Cloud certifications are valid for two years from the date of certification. Recertification is accomplished by retaking the exam during the recertification eligibility time period and achieving a passing score. You may attempt recertification starting 60 days prior to your certification expiration date.

Following your booking, a confirmation message will be sent to all participants, ensuring you're well-informed of your successful enrollment. Calendar placeholders will also be dispatched to assist you in scheduling your commitments around the course. Rest assured, all course materials and access to necessary labs or platforms will be provided no later than one week before the course begins, allowing you ample time to prepare and engage fully with the learning experience ahead.

Our comprehensive training package includes all the necessary materials and resources to facilitate a full learning experience. Enrollees will be provided with detailed course content, encompassing a wide array of topics to ensure a thorough understanding of the subject matter. Additionally, participants will receive a certificate of completion to recognize their dedication and hard work. It's important to note that while the course fee covers all training materials and experiences, the examination fee for certification is not included but can be purchased separately.

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