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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. Designing for high availability and fault tolerance
  • 2. Optimizing for latency and throughput
  • 3. Selecting appropriate compute and deployment platforms
- 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. Planning logging, metrics, and tracing strategies
  • 2. Designing for debugging and troubleshooting
Integrating applications with Google Cloud services21%- 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
- 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
- Implementing application performance monitoring
  • 1. Instrumenting code for metrics and traces
  • 2. Using operations suite tools
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
- Setting up development environments
  • 1. Using Cloud SDK, Cloud Code, Cloud Shell, and Cloud Workstations
  • 2. Emulating Google Cloud services locally
- Testing applications
  • 1. Using AI-assisted development tools
  • 2. Writing unit, integration, and end-to-end tests
  • 3. Automating testing in CI/CD workflows
Configuring cloud-native applications for deployment24%- Deploying applications to Google Kubernetes Engine
  • 1. Managing configurations and secrets
  • 2. Configuring scaling, auto-scaling, and updates
  • 3. Creating and managing workloads and services
- 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

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Google Certified Professional - Cloud Developer Sample Questions (Q163-Q168):

NEW QUESTION # 163
You are developing an application that will allow users to read and post comments on news articles. You want to configure your application to store and display user-submitted comments using Firestore. How should you design the schema to support an unknown number of comments and articles?

Answer: C


NEW QUESTION # 164
You are configuring a continuous integration pipeline using Cloud Build to automate the deployment of new container images to Google Kubernetes Engine (GKE). The pipeline builds the application from its source code, runs unit and integration tests in separate steps, and pushes the container to Container Registry. The application runs on a Python web server.
The Dockerfile is as follows:
FROM python:3.7-alpine -
COPY . /app -
WORKDIR /app -
RUN pip install -r requirements.txt
CMD [ "gunicorn", "-w 4", "main:app" ]
You notice that Cloud Build runs are taking longer than expected to complete. You want to decrease the build time. What should you do? (Choose two.)

Answer: A,B

Explanation:
Explanation
https://cloud.google.com/build/docs/optimize-builds/increase-vcpu-for-builds By default, Cloud Build runs your builds on a standard virtual machine (VM). In addition to the standard VM, Cloud Build provides several high-CPU VM types to run builds. To increase the speed of your build, select a machine with a higher vCPU to run builds. Keep in mind that although selecting a high vCPU machine increases your build speed, it may also increase the startup time of your build as Cloud Build only starts non-standard machines on demand.
https://cloud.google.com/build/docs/optimize-builds/speeding-up-builds#using_a_cached_docker_image The easiest way to increase the speed of your Docker image build is by specifying a cached image that can be used for subsequent builds. You can specify the cached image by adding the --cache-from argument in your build config file, which will instruct Docker to build using that image as a cache source.


NEW QUESTION # 165
Your team is building an application for a financial institution. The application's frontend runs on Compute Engine, and the data resides in Cloud SQL and one Cloud Storage bucket. The application will collect data containing PII, which will be stored in the Cloud SQL database and the Cloud Storage bucket. You need to secure the PII data. What should you do?

Answer: A

Explanation:
Without using VPC-SC, the PII data is not secure from exfiltration. So that leaves only C, and D as possible valid responses. However, D can be eliminated because both the Cloud SQL instance and and Cloud Storage bucket must be within the same perimeter, which leaves C and the valid answer.


NEW QUESTION # 166
Your application is running on Compute Engine and is showing sustained failures for a small number of requests. You have narrowed the cause down to a single Compute Engine instance, but the instance is unresponsive to SSH.
What should you do next?

Answer: A

Explanation:
https://cloud.google.com/compute/docs/troubleshooting/troubleshooting-
ssh#debug_with_serial_console


NEW QUESTION # 167
You are developing a web application that will be accessible over both HTTP and HTTPS and will run on Compute Engine instances. On occasion, you will need to SSH from your remote laptop into one of the Compute Engine instances to conduct maintenance on the app. How should you configure the instances while following Google-recommended best practices?

Answer: B


NEW QUESTION # 168
......

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