Google GCP-PCD (Professional Cloud Developer) Certification Exam Syllabus

GCP-PCD Dumps Questions, GCP-PCD PDF, Professional Cloud Developer Exam Questions PDF, Google GCP-PCD Dumps Free, Professional Cloud Developer Official Cert Guide PDFThe Google GCP-PCD exam preparation guide is designed to provide candidates with necessary information about the Professional Cloud Developer exam. It includes exam summary, sample questions, practice test, objectives and ways to interpret the exam objectives to enable candidates to assess the types of questions-answers that may be asked during the Google Cloud Platform - Professional Cloud Developer (GCP-PCD) exam.

It is recommended for all the candidates to refer the GCP-PCD objectives and sample questions provided in this preparation guide. The Google Professional Cloud Developer certification is mainly targeted to the candidates who want to build their career in Cloud domain and demonstrate their expertise. We suggest you to use practice exam listed in this cert guide to get used to with exam environment and identify the knowledge areas where you need more work prior to taking the actual Google Professional Cloud Developer exam.

Google GCP-PCD Exam Summary:

Exam Name
Google Professional Cloud Developer
Exam Code GCP-PCD
Exam Price $200 USD
Duration 120 minutes
Number of Questions 50-60
Passing Score Pass / Fail (Approx 70%)
Recommended Training / Books Google Cloud training
Google Cloud documentation
Google Cloud solutions
Schedule Exam PEARSON VUE
Sample Questions Google GCP-PCD Sample Questions
Recommended Practice Google Cloud Platform - Professional Cloud Developer (GCP-PCD) Practice Test

Google Professional Cloud Developer Syllabus:

Section Objectives

Designing highly scalable, available, and reliable cloud-native applications

Designing high-performing applications and APIs. Considerations include: - Microservices
- Scaling velocity characteristics/trade-offs of IaaS (infrastructure as a service) vs. CaaS (container as a service) vs. PaaS (platform as a service)
- Geographic distribution of Google Cloud services (e.g., latency, regional services, zonal services)
- Defining a key structure for high-write applications using Cloud Storage, Cloud Bigtable, Cloud Spanner, or Cloud SQL
- User session management
- Caching solutions
- Deploying and securing API services
- Loosely coupled asynchronous applications (e.g., Apache Kafka, Pub/Sub)
- Graceful shutdown on platform termination
- Google-recommended practices and documentation
Designing secure applications. Considerations include: - Implementing requirements that are relevant for applicable regulations (e.g., data wipeout)
- Security mechanisms that protect services and resources
- Security mechanisms that secure/scan application binaries and manifests
- Storing and rotating application secrets and keys (e.g., Cloud KMS, HashiCorp Vault)
- Authenticating to Google services (e.g., application default credentials, JSON Web Token (JWT), OAuth 2.0)
- IAM roles for users/groups/service accounts
- Securing service-to-service communications (e.g., service mesh, Kubernetes network policies, and Kubernetes namespaces)
- Running services with least privileged access (e.g., Workload Identity)
- Certificate-based authentication (e.g., SSL, mTLS)
- Google-recommended practices and documentation
Managing application data. Considerations include: - Defining database schemas for Google-managed databases (e.g., Firestore, Cloud Spanner, Cloud Bigtable, Cloud SQL)
- Choosing data storage options based on use case considerations, such as:
  • Time-limited access to objects
  • Data retention requirements
  • Structured vs. unstructured data
  • Strong vs. eventual consistency
  • Data volume
  • Frequency of data access in Cloud Storage

- Google-recommended practices and documentation

Application modernization. Considerations include: - Using managed services
- Refactoring a monolith to microservices
- Designing stateless, horizontally scalable services
- Google-recommended practices and documentation

Building and Testing Applications

Setting up your local development environment. Considerations include: - Emulating Google Cloud services for local application development
- Creating Google Cloud projects
- Using the command-line interface (CLI), Google Cloud Console, and Cloud Shell tools
- Using developer tooling (e.g., Cloud Code, Skaffold)
Writing efficient code. Considerations include: - Algorithm design
- Modern application patterns
- Software development methodologies
- Debugging and profiling code
Testing. Considerations include: - Unit testing
- Integration testing
- Performance testing
- Load testing
Building. Considerations include: - Source control management
- Creating secure container images from code
- Developing a continuous integration pipeline using services (e.g., Cloud Build, Container Registry) that construct deployment artifacts
- Reviewing and improving continuous integration pipeline efficiency

Deploying applications

Recommend appropriate deployment strategies using the appropriate tools (e.g., Cloud Build, Spinnaker, Tekton, Anthos Configuration Manager) for the target compute environment (e.g., Compute Engine, Google Kubernetes Engine). Considerations include: - Blue/green deployments
- Traffic-splitting deployments
- Rolling deployments
- Canary deployments
Deploying applications and services on Compute Engine. Considerations include: - Installing an application into a virtual machine (VM)
- Managing service accounts for VMs
- Bootstrapping applications
- Exporting application logs and metrics
- Managing Compute Engine VM images and binaries
Deploying applications and services to Google Kubernetes Engine (GKE). Considerations include: - Deploying a containerized application to GKE
- Managing Kubernetes RBAC and Google Cloud IAM relationships
- Configuring Kubernetes namespaces
- Defining workload specifications (e.g., resource requirements)
- Building a container image using Cloud Build
- Configuring application accessibility to user traffic and other services
- Managing container life cycle
- Define Kubernetes resources and configurations
Deploying a Cloud Function. Considerations include: - Cloud Functions that are triggered via an event from Google Cloud services (e.g., Pub/Sub, Cloud Storage objects)
- Cloud Functions that are invoked via HTTP
- Securing Cloud Functions
Using service accounts. Considerations include: - Creating a service account according to the principle of least privilege
- Downloading and using a service account private key file

Integrating Google Cloud Services

Integrating an application with Data and Storage services. Considerations include: - Read/write data to/from various databases (e.g., SQL)
- Connecting to a data store (e.g., Cloud SQL, Cloud Spanner, Firestore, Cloud Bigtable)
- Writing an application that publishes/consumes data asynchronously (e.g., from Pub/Sub)
- Storing and retrieving objects from Cloud Storage
Integrating an application with compute services. Considerations include: - Implementing service discovery in GKE and Compute Engine
- Reading instance metadata to obtain application configuration
- Authenticating users by using OAuth2.0 Web Flow and Identity-Aware Proxy
- Authenticating to Cloud APIs with Workload Identity
Integrating Cloud APIs with applications. Considerations include: - Enabling a Cloud API
- Making API calls using supported options (e.g., Cloud Client Library, REST API or gRPC, APIs 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

Managing Application Performance Monitoring

Managing Compute Engine VMs. Considerations include: - Debugging a custom VM image using the serial port
- Diagnosing a failed Compute Engine VM startup
- Sending logs from a VM to Cloud Logging
- Viewing and analyzing logs
- Inspecting resource utilization over time
Managing Google Kubernetes Engine workloads. Considerations include: - Configuring logging and monitoring
- Analyzing container life cycle events (e.g., CrashLoopBackOff, ImagePullErr)
- Viewing and Analyzing logs
- Writing and exporting custom metrics
- Using external metrics and corresponding alerts
- Configuring workload autoscaling
Troubleshooting application performance. Considerations include: - Creating a monitoring dashboard
- Writing custom metrics and creating log-based metrics
- Using Cloud Debugger
- Reviewing stack traces for error analysis
- Exporting logs from Google Cloud
- Viewing logs in the Google Cloud Console
- Reviewing application performance (e.g., Cloud Trace, Prometheus, OpenTelemetry)
- Monitoring and profiling a running application
- Using documentation, forums, and Google Cloud support
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