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Google Professional-Cloud-Developer Exam Syllabus Topics:

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

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Google Certified Professional - Cloud Developer Sample Questions (Q230-Q235):

NEW QUESTION # 230
You are running an application on App Engine that you inherited. You want to find out whether the application is using insecure binaries or is vulnerable to XSS attacks.
Which service should you use?

Answer: A

Explanation:
Explanation/Reference: https://cloud.google.com/security-scanner


NEW QUESTION # 231
Your development team has built several Cloud Functions using Java along with corresponding integration and service tests. You are building and deploying the functions and launching the tests using Cloud Build.
Your Cloud Build job is reporting deployment failures immediately after successfully validating the code.
What should you do?

Answer: D

Explanation:
https://cloud.google.com/build/docs/securing-builds/configure-access-for-cloud-build-service-account


NEW QUESTION # 232
You have two tables in an ANSI-SQL compliant database with identical columns that you need to quickly combine into a single table, removing duplicate rows from the result set.
What should you do?

Answer: D

Explanation:
Reference: https://www.techonthenet.com/sql/union_all.php


NEW QUESTION # 233
Technician Tommy is replacing a logic board on a Mac mini. Which of the following should he hold when handling the logic board?

Answer: C

Explanation:
Explanation/Reference: https://www.ifixit.com/Guide/Mac+mini+Late+2014+Logic+Board+Replacement/33077


NEW QUESTION # 234
You are developing an ecommerce application that stores customer, order, and inventory data as relational tables inside Cloud Spanner. During a recent load test, you discover that Spanner performance is not scaling linearly as expected. Which of the following is the cause?

Answer: B


NEW QUESTION # 235
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