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Google Professional-Cloud-Developer Exam Syllabus Topics:

SectionWeightObjectives
Configuring cloud-native applications for deployment24%- Deploying applications to Cloud Run
  • 1. Integrating with event-driven services
  • 2. Deploying from source code or containers
  • 3. Configuring scaling, networking, and security
- Deploying to App Engine or Cloud Functions
  • 1. Configuring serverless deployments
  • 2. Managing versions and traffic splitting
- Deploying applications to Google Kubernetes Engine
  • 1. Creating and managing workloads and services
  • 2. Configuring scaling, auto-scaling, and updates
  • 3. Managing configurations and secrets
Designing highly scalable, secure, and reliable cloud-native applications32%- Designing high-performing applications and APIs
  • 1. Designing for high availability and fault tolerance
  • 2. Selecting appropriate compute and deployment platforms
  • 3. Optimizing for latency and throughput
- Designing secure applications
  • 1. Securing data in transit and at rest
  • 2. Implementing authentication and authorization mechanisms
  • 3. Applying security best practices and compliance requirements
- Designing for observability and monitoring
  • 1. Designing for debugging and troubleshooting
  • 2. Planning logging, metrics, and tracing strategies
Building and testing applications23%- Setting up development environments
  • 1. Emulating Google Cloud services locally
  • 2. Using Cloud SDK, Cloud Code, Cloud Shell, and Cloud Workstations
- Testing applications
  • 1. Writing unit, integration, and end-to-end tests
  • 2. Using AI-assisted development tools
  • 3. Automating testing in CI/CD workflows
- Building applications and containers
  • 1. Implementing containerization best practices
  • 2. Using Cloud Build and Artifact Registry
  • 3. Configuring build pipelines and provenance
Integrating applications with Google Cloud services21%- Consuming Google Cloud APIs and services
  • 1. Using client libraries, REST, and gRPC
  • 2. Integrating with AI/ML and generative AI services
  • 3. Enabling and managing APIs
- Implementing application performance monitoring
  • 1. Using operations suite tools
  • 2. Instrumenting code for metrics and traces
- Integrating with data and storage services
  • 1. Working with relational, NoSQL, and in-memory databases
  • 2. Using object storage and file storage solutions
  • 3. Implementing messaging and event streaming

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Google Certified Professional - Cloud Developer 認定 Professional-Cloud-Developer 試験問題 (Q77-Q82):

質問 # 77
Your application is deployed on hundreds of Compute Engine instances in a managed instance group (MIG) in multiple zones. You need to deploy a new instance template to fix a critical vulnerability immediately but must avoid impact to your service. What setting should be made to the MIG after updating the instance template?

正解：C

解説：
https://cloud.google.com/compute/docs/instance-groups/rolling-out-updates-to-managed- instance-groups#minimum_wait_time


質問 # 78
You are tasked with using C++ to build and deploy a microservice for an application hosted on Google Cloud. The code needs to be containerized and use several custom software libraries that your team has built. You do not want to maintain the underlying infrastructure of the application.
How should you deploy the microservice?

正解：A


質問 # 79
For this question, refer to the HipLocal case study.
How should HipLocal increase their API development speed while continuing to provide the QA team with a stable testing environment that meets feature requirements?

正解：D


質問 # 80
You want to use the Stackdriver Logging Agent to send an application's log file to Stackdriver from a Compute Engine virtual machine instance.
After installing the Stackdriver Logging Agent, what should you do first?

正解：D

解説：
https://cloud.google.com/logging/docs/agent/configuration


質問 # 81
You are building an API that will be used by Android and iOS apps. The API must:
- Support HTTPs
- Minimize bandwidth cost
- Integrate easily with mobile apps
Which API architecture should you use?

正解：D

解説：
gRPC architectural style has promising features that can (and should) be explored. It is an excellent option for working with multi-language systems, real-time streaming, and for instance, when operating an IoT system that requires light-weight message transmission such as the serialized Protobuf messages allow. Moreover, gRPC should also be considered for mobile applications since they do not need a browser and can benefit from smaller messages, preserving mobiles' processors' speed.


質問 # 82
......

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