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Google Professional-Cloud-Developer Exam Syllabus Topics:

SectionWeightObjectives
Designing highly scalable, secure, and reliable cloud-native applications32%- 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
- Designing for observability and monitoring
  • 1. Planning logging, metrics, and tracing strategies
  • 2. Designing for debugging and troubleshooting
- Designing secure applications
  • 1. Implementing authentication and authorization mechanisms
  • 2. Applying security best practices and compliance requirements
  • 3. Securing data in transit and at rest
Building and testing applications23%- Building applications and containers
  • 1. Implementing containerization best practices
  • 2. Using Cloud Build and Artifact Registry
  • 3. Configuring build pipelines and provenance
- Testing applications
  • 1. Writing unit, integration, and end-to-end tests
  • 2. Using AI-assisted development tools
  • 3. Automating testing in CI/CD workflows
- Setting up development environments
  • 1. Using Cloud SDK, Cloud Code, Cloud Shell, and Cloud Workstations
  • 2. Emulating Google Cloud services locally
Configuring cloud-native applications for deployment24%- Deploying applications to Google Kubernetes Engine
  • 1. Configuring scaling, auto-scaling, and updates
  • 2. Creating and managing workloads and services
  • 3. Managing configurations and secrets
- Deploying applications to Cloud Run
  • 1. Configuring scaling, networking, and security
  • 2. Deploying from source code or containers
  • 3. Integrating with event-driven services
- Deploying to App Engine or Cloud Functions
  • 1. Managing versions and traffic splitting
  • 2. Configuring serverless deployments
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 Sample Questions (Q136-Q141):

NEW QUESTION # 136
You work on an application that relies on Cloud Spanner as its main datastore. New application features have occasionally caused performance regressions. You want to prevent performance issues by running an automated performance test with Cloud Build for each commit made. If multiple commits are made at the same time, the tests might run concurrently. What should you do?

Answer: C

Explanation:
Since the testing needs to accommodate scenarios where multiple commits are made simultaneously, and hence multiple tests might run concurrently, the testing environment should support isolated and independent testing instances to avoid interference among tests. Given these requirements, using the Cloud Spanner emulator would not be the best choice for this scenario. The emulator is primarily suited for local development, unit, and integration testing, and is not built for production-scale performance testing. It may not accurately replicate performance characteristics at scale or under load, which are crucial aspects in this case.


NEW QUESTION # 137
You are building an application that uses a distributed microservices architecture. You want to measure the performance and system resource utilization in one of the microservices written in Java. What should you do?

Answer: B

Explanation:
https://cloud.google.com/profiler/docs/profiling-java
https://cloud.google.com/appengine/docs/legacy/standard/java/microservice-performance


NEW QUESTION # 138
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?

Answer: B


NEW QUESTION # 139
Case Study
Company Overview
HipLocal is a community application designed to facilitate communication between people in close proximity. It is used for event planning and organizing sporting events, and for businesses to connect with their local communities. HipLocal launched recently in a few neighborhoods in Dallas and is rapidly growing into a global phenomenon. Its unique style of hyper-local community communication and business outreach is in demand around the world.
Executive Statement
We are the number one local community app; it's time to take our local community services global. Our venture capital investors want to see rapid growth and the same great experience for new local and virtual communities that come online, whether their members are 10 or 10000 miles away from each other.
Solution Concept
HipLocal wants to expand their existing service, with updated functionality, in new regions to better serve their global customers. They want to hire and train a new team to support these regions in their time zones. They will need to ensure that the application scales smoothly and provides clear uptime data.
Existing Technical Environment
HipLocal's environment is a mix of on-premises hardware and infrastructure running in Google Cloud Platform.
The HipLocal team understands their application well, but has limited experience in global scale applications.
Their existing technical environment is as follows:
* Existing APIs run on Compute Engine virtual machine instances hosted in GCP.
* State is stored in a single instance MySQL database in GCP.
* Data is exported to an on-premises Teradata/Vertica data warehouse.
* Data analytics is performed in an on-premises Hadoop environment.
* The application has no logging.
* There are basic indicators of uptime; alerts are frequently fired when the APIs are unresponsive.
Business Requirements
HipLocal's investors want to expand their footprint and support the increase in demand they are seeing. Their requirements are:
* Expand availability of the application to new regions.
* Increase the number of concurrent users that can be supported.
* Ensure a consistent experience for users when they travel to different regions.
* Obtain user activity metrics to better understand how to monetize their product.
* Ensure compliance with regulations in the new regions (for example, GDPR).
* Reduce infrastructure management time and cost.
* Adopt the Google-recommended practices for cloud computing.
Technical Requirements
* The application and backend must provide usage metrics and monitoring.
* APIs require strong authentication and authorization.
* Logging must be increased, and data should be stored in a cloud analytics platform.
* Move to serverless architecture to facilitate elastic scaling.
* Provide authorized access to internal apps in a secure manner.
HipLocal wants to reduce the number of on-call engineers and eliminate manual scaling.
Which two services should they choose? (Choose two.)

Answer: A,D

Explanation:
Explanation


NEW QUESTION # 140
You developed a Python script that retrieves information from files that are uploaded to Cloud Storage and writes the information to Bigtable. You have completed testing on your local environment and created the python-script service account with the Bigtable User IAM role. You want to deploy the code with the appropriate authentication while following Google-recommended practices. What should you do?

Answer: A

Explanation:
Deploying the Python script to Cloud Run with a Cloud Scheduler trigger allows for flexible, managed execution, while maintaining security best practices. Using IAM binding for authentication avoids the need for managing service account keys, aligning with Google- recommended practices for security by using Application Default Credentials (ADC). Cloud Scheduler can trigger the Cloud Run service at the desired frequency, allowing the script to process new files as needed.
Using Cloud Functions with a Cloud Storage trigger could work, but Cloud Run provides more flexibility and control for containerized applications. Additionally, creating a service account key for authentication is not recommended because managing and securing keys is more complex and less secure than using IAM roles and ADC.


NEW QUESTION # 141
......

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