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Google Professional-Cloud-Developer Exam Syllabus Topics:

Section	Objectives
Manage application performance monitoring	- Monitor and troubleshoot applications 1. Optimize application performance 2. Configure logging and tracing 3. Analyze metrics and alerts
Integrate applications with Google Cloud services	- Integrate with data and storage services 1. Use messaging and event-driven services 2. Integrate with Google Cloud APIs 3. Implement authentication and authorization
Design scalable, available, and reliable cloud-native applications	- Design highly performant applications and APIs 1. Design resilient and scalable application architectures 2. Design secure applications 3. Choose storage options for application data
Deploy applications	- Deploy applications to Google Cloud 1. Deploy applications to Google Kubernetes Engine 2. Manage application rollout strategies 3. Deploy applications using Cloud Run
Build and test applications	- Manage development environments 1. Implement CI/CD pipelines 2. Perform application testing 3. Build application artifacts

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최신 Cloud Developer Professional-Cloud-Developer 무료샘플문제 (Q205-Q210):

질문 # 205
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.)

A. Deploy a Container Registry on a Compute Engine VM in a VPC, and use it to store the final images.
B. Change the base image in the Dockerfile to ubuntu:latest, and install Python 3.7 using a package manager utility.
C. Store application source code on Cloud Storage, and configure the pipeline to use gsutil to download the source code.
D. Select a virtual machine (VM) size with higher CPU for Cloud Build runs.
E. Cache the Docker image for subsequent builds using the -- cache-from argument in your build config file.

정답：D,E

설명：
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.

질문 # 206
You plan to deploy a new Go application to Cloud Run. The source code is stored in Cloud Source Repositories. You need to configure a fully managed, automated, continuous deployment pipeline that runs when a source code commit is made. You want to use the simplest deployment solution. What should you do?

A. Configure continuous deployment of new revisions from a source repository for Cloud Run using buildpacks.
B. Use Cloud Build with a trigger configured to run the container build and deploy process for each source code commit to Cloud Source Repositories.
C. Configure a Jenkins trigger to run the container build and deploy process for each source code commit to Cloud Source Repositories.
D. Configure a cron job on your workstations to periodically run gcloud run deploy --source in the working directory.

정답：B

설명：
https://cloud.google.com/run/docs/continuous-deployment-with-cloud-build Cloud Build is a fully managed, scalable, and efficient service provided by Google Cloud that allows you to automate your software delivery pipeline, including building, testing, and deploying applications. By using a trigger with Cloud Build, you can automatically build and deploy your Go application to Cloud Run whenever a source code commit is made in Cloud Source Repositories.
This provides a simple, fully managed solution for continuous deployment, and eliminates the need for manual processes or external tools like Jenkins.

질문 # 207
You are developing a flower ordering application Currently you have three microservices.
* Order Service (receives the orders).
* Order Fulfillment Service (processes the orders).
* Notification Service (notifies the customer when the order is filled).
You need to determine how the services will communicate with each other. You want incoming orders to be processed quickly and you need to collect order information for fulfillment. You also want to make sure orders are not lost between your services and are able to communicate asynchronously. How should the requests be processed?

정답：C

질문 # 208
Your company's corporate policy states that there must be a copyright comment at the very beginning of all source files. You want to write a custom step in Cloud Build that is triggered by each source commit. You need the trigger to validate that the source contains a copyright and add one for subsequent steps if not there.
What should you do?

A. Build a new Docker container that examines the files in /workspace and then checks and adds a copyright for each source file. Changed files are explicitly committed back to the source repository.
B. Build a new Docker container that examines the files in a Cloud Storage bucket and then checks and adds a copyright for each source file. Changed files are explicitly committed back to the source repository.
C. Build a new Docker container that examines the files in a Cloud Storage bucket and then checks and adds a copyright for each source file. Changed files are written back to the Cloud Storage bucket.
D. Build a new Docker container that examines the files in /workspace and then checks and adds a copyright for each source file. Changed files do not need to be committed back to the source repository.

정답：A

설명：
https://cloud.google.com/build/docs/configuring-builds/pass-data-between-steps#passing_data_using_workspace To pass data between build steps, store the assets produced by the build step in /workspace and these assets will be available to any subsequent build steps.

질문 # 209
You are developing an online gaming platform as a microservices application on Google Kubernetes Engine (GKE). Users on social media are complaining about long loading times for certain URL requests to the application. You need to investigate performance bottlenecks in the application and identify which HTTP requests have a significantly high latency span in user requests. What should you do9

A. Configure GKE workload metrics using kubect1. Select all Pods to send their metrics to Cloud Monitoring. Create a custom dashboard of application metrics in Cloud Monitoring to determine performance bottlenecks of your GKE cluster.
B. Install tcpdiimp on your GKE nodes. Run tcpdunm-- to capture network traffic over an extended period of time to collect data. Analyze the data files using Wireshark to determine the cause of high latency
C. Instrument your microservices by installing the Open Telemetry tracing package. Update your application code to send traces to Trace for inspection and analysis. Create an analysis report on Trace to analyze user requests
D. Update your microservices lo log HTTP request methods and URL paths to STDOUT Use the logs router to send container logs to Cloud Logging. Create fillers in Cloud Logging to evaluate the latency of user requests across different methods and URL paths.

정답：A

질문 # 210
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