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Google Professional-Cloud-Developer Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Integrating applications with Google Cloud services	21%	- Integrating with data and storage services 1. Using object storage and file storage solutions 2. Implementing messaging and event streaming 3. Working with relational, NoSQL, and in-memory databases - Implementing application performance monitoring 1. Using operations suite tools 2. Instrumenting code for metrics and traces - Consuming Google Cloud APIs and services 1. Enabling and managing APIs 2. Integrating with AI/ML and generative AI services 3. Using client libraries, REST, and gRPC
Topic 2: Designing highly scalable, secure, and reliable cloud-native applications	32%	- Designing high-performing applications and APIs 1. Optimizing for latency and throughput 2. Selecting appropriate compute and deployment platforms 3. Designing for high availability and fault tolerance - Designing secure applications 1. Securing data in transit and at rest 2. Applying security best practices and compliance requirements 3. Implementing authentication and authorization mechanisms - Designing for observability and monitoring 1. Designing for debugging and troubleshooting 2. Planning logging, metrics, and tracing strategies
Topic 3: Configuring cloud-native applications for deployment	24%	- Deploying applications to Google Kubernetes Engine 1. Configuring scaling, auto-scaling, and updates 2. Managing configurations and secrets 3. Creating and managing workloads and services - Deploying to App Engine or Cloud Functions 1. Managing versions and traffic splitting 2. Configuring serverless deployments - Deploying applications to Cloud Run 1. Deploying from source code or containers 2. Integrating with event-driven services 3. Configuring scaling, networking, and security
Topic 4: Building and testing applications	23%	- Testing applications 1. Automating testing in CI/CD workflows 2. Writing unit, integration, and end-to-end tests 3. Using AI-assisted development tools - Building applications and containers 1. Configuring build pipelines and provenance 2. Using Cloud Build and Artifact Registry 3. Implementing containerization best practices - Setting up development environments 1. Using Cloud SDK, Cloud Code, Cloud Shell, and Cloud Workstations 2. Emulating Google Cloud services locally

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Google Certified Professional - Cloud Developer Sample Questions (Q37-Q42):

NEW QUESTION # 37
You are a developer at a large organization. Your team uses Git for source code management (SCM). You want to ensure that your team follows Google-recommended best practices to manage code to drive higher rates of software delivery. Which SCM process should your team use?

A. Each developer commits their code to the main branch before each product release, conducts testing, and rolls back if integration issues are detected.
B. Each group of developers creates a feature branch from the main branch for their work, commits their changes to their branch, and merges their code into the main branch before each major release.
C. Each group of developers copies the repository, commits their changes to their repository, and merges their code into the main repository before each product release.
D. Each developer creates a branch for their own work, commits their changes to their branch, and merges their code into the main branch daily.

Answer: D

Explanation:
Following Google-recommended practices, frequent integration is key to reducing integration issues and driving higher rates of software delivery. By having each developer create their own branch, make commits, and merge into the main branch daily, the team ensures that integration happens continuously. This trunk-based development approach encourages regular testing and validation, making it easier to detect issues early and avoid large, complex integrations before releases.
This practice also aligns with CI/CD principles, which emphasize small, frequent commits and continuous integration to maintain high code quality and accelerate development velocity.

NEW QUESTION # 38
Your team develops services that run on Google Kubernetes Engine. Your team's code is stored in Cloud Source Repositories. You need to quickly identify bugs in the code before it is deployed to production. You want to invest in automation to improve developer feedback and make the process as efficient as possible.
What should you do?

A. Use Spinnaker to automate deploying container images to the production environment.
B. Use Spinnaker to automate building container images from code based on Git tags.
C. Use Cloud Build to automate building container images from code based on Git tags.
D. Use Cloud Build to automate building container images from code based on forked versions.

Answer: B

Explanation:
Reference: https://spinnaker.io/docs/guides/tutorials/codelabs/kubernetes-v2-source-to-prod/

NEW QUESTION # 39
You recently developed a new service on Cloud Run. The new service authenticates using a custom service and then writes transactional information to a Cloud Spanner database. You need to verify that your application can support up to 5,000 read and 1,000 write transactions per second while identifying any bottlenecks that occur. Your test infrastructure must be able to autoscale. What should you do?

A. Build a test harness to generate requests and deploy it to Cloud Run. Analyze the VPC Flow Logs using Cloud Logging.
B. Create a Compute Engine instance that uses a LAMP stack image from the Marketplace, and use Apache Bench to generate load tests against the service. Analyze the results using Cloud Trace.
C. Create a Cloud Task to generate a test load. Use Cloud Scheduler to run 60,000 Cloud Task transactions per minute for 10 minutes. Analyze the results using Cloud Monitoring.
D. Create a Google Kubernetes Engine cluster running the Locust or JMeter images to dynamically generate load tests. Analyze the results using Cloud Trace.

Answer: D

Explanation:
https://cloud.google.com/architecture/distributed-load-testing-using-gke

NEW QUESTION # 40
You are designing a deployment technique for your new applications on Google Cloud. As part of your deployment planning, you want to use live traffic to gather performance metrics for both new and existing applications. You need to test against the full production load prior to launch. What should you do?

A. Use blue/green deployment
B. Use rolling updates deployment
C. Use canary deployment
D. Use A/B testing with traffic mirroring during deployment

Answer: D

Explanation:
You need to test against the full production load prior to launch" It's impossible with canary.
A/B testing with traffic mirroring during deployment" is the only one possibility we have to test the entire traffic before the roll out.

NEW QUESTION # 41
Wendell explains that his iPhone X is dropping calls. Which of the following is an open question that would best help you further evaluate the issue with Wendell?

A. Are you in an elevator or a parking garage when the iPhone drops the calls?
B. How is the rest of your experience with the iPhone, aside from the dropped calls?
C. Could you please tell me about the situations when your iPhone drops calls?
D. Do you find that you are driving in a car when these dropped calls occur?

Answer: C

NEW QUESTION # 42
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