Google Cloud Certified – Professional Cloud Developer Practice Exam

Google Cloud Certified – Professional Cloud Developer Practice Exam

 

A Professional Cloud Developer constructs resilient and scalable applications utilizing Google-endorsed tools and industry best practices. This practitioner is adept in developing cloud-native applications, utilizing developer tools, managed services, and modern databases. Additionally, they demonstrate proficiency in at least one general-purpose programming language and implement instrumentation in their code to generate metrics, logs, and traces. The Professional Cloud Developer examination evaluates your proficiency in the following areas:

• Designing cloud-native applications that are highly scalable, available, and reliable.
• Developing and testing applications.
• Deploying applications effectively.
• Integrating Google Cloud services seamlessly.
• Managing applications post-deployment.

 

Who should take the Google Professional Cloud Developer exam?

The Google Professional Cloud Developer exam is ideal for individuals with the following knowledge:

• Industry experience exceeding three years encompasses hands-on involvement in developing and deploying cloud applications, extending beyond mere theoretical comprehension of cloud concepts.
• Furthermore, a minimum of one year of experience in designing and managing solutions utilizing Google Cloud signifies a tangible grasp of applying GCP services within actual operational contexts.

 

Exam Details

• Exam Name: Professional Cloud Developer
• Exam Duration: 2 hours
• Exam Language: English and Japanese
• Number of Questions: 50-60

 

Course Outline

The Google Professional Cloud Developer exam covers the following topics - 

Section 1: Learn about 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: Understand about 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: Learn about 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: Understanding 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)
• Using service accounts to make Cloud API calls
• Integrating with Google Cloud’s operations suite