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Topics of Google Professional Cloud Developer Exam

Candidates must know the exam topics before they start of preparation.because it will really help them in hitting the core.Our Google Professional Cloud Developer Dumps will include the following topics:

1. Designing highly scalable, available, and reliable cloud-native applications

Designing high-performing applications and APIs

Deploying and securing API services
Geographic distribution of Google Cloud services (e.g., latency, regional services, zonal services)
Loosely coupled applications using asynchronous Cloud Pub/Sub events
Caching solutions
Google-recommended practices and documentation
Microservices
User session management
Evaluating different services and technologies

Designing secure applications

Security mechanisms that protect services and resources
IAM roles for users/groups/service accounts
Storing and rotating application secrets using Cloud KMS
Authenticating to Google services (e.g., application default credentials, JWT, OAuth 2.0)
Securing service-to-service communications (e.g., service mesh, Kubernetes network policies, and Kubernetes namespaces)
Certificate-based authentication (e.g., SSL, mTLS)
Google-recommended practices and documentation

Managing application data

Frequency of data access in Cloud Storage
Structured vs. unstructured data
Strong vs. eventual consistency
Following Google-recommended practices and documentation
Data volume

Refactoring applications to migrate to Google Cloud

Google-recommended practices and documentation
Migrating a monolith to microservices
Using managed services

2 Building and Testing Applications

Setting up your local development environment

Creating Google Cloud projects
Emulating Google Cloud services for local application development

Writing code

Modern application patterns
Algorithm design
Agile software development
Efficiency
Unit testing

Testing

Performance testing
Load testing
Integration testing

Building

Reviewing and improving continuous integration pipeline efficacy
Creating a Cloud Source Repository and committing code to it
Developing a continuous integration pipeline using services (e.g., Cloud Build, Container Registry) that construct deployment artifacts
Creating container images from code

3 Deploying applications

Recommend appropriate deployment strategies for the target compute environment (Compute Engine, Google Kubernetes Engine). Strategies include:

Rolling deployments
Traffic-splitting deployments
Blue/green deployments
Canary deployments

Deploying applications and services on Compute Engine

Modifying the VM service account
Exporting application logs and metrics
Installing an application into a VM
Managing Compute Engine VM images and binaries
Manually updating dependencies on a VM

Deploying applications and services to Google Kubernetes Engine (GKE)

Managing container lifecycle
Configuring application accessibility to user traffic and other services
Defining workload specifications (e.g., resource requirements)
Managing Kubernetes RBAC and Google Cloud IAM relationship

Deploying a Cloud Function

Securing Cloud Functions
Cloud Functions that are invoked via HTTP
Cloud Functions that are triggered via an event (e.g., Cloud Pub/Sub events, Cloud Storage object change notification events)

Using service accounts

Creating a service account according to the principle of least privilege
Downloading and using a service account private key file

4 Integrating Google Cloud Platform Services

Integrating an application with data and storage services

Read/write data to/from various databases (e.g., SQL, JDBC)
Connecting to a data store (e.g., Cloud SQL, Cloud Spanner, Cloud Firestore, Cloud Bigtable)
Storing and retrieving objects from Cloud Storage
Writing an application that publishes/consumes data asynchronously (e.g., from Cloud Pub/Sub)
Using the command-line interface (CLI), Google Cloud Console, and Cloud Shell tools

Integrating an application with compute services

Implementing service discovery in Google Kubernetes Engine and Compute Engine
Reading instance metadata to obtain application configuration
Using the command-line interface (CLI), Google Cloud Console, and Cloud Shell tools
Authenticating users by using OAuth2.0 Web Flow and Identity Aware Proxy

Integrating Google Cloud APIs with applications

Using service accounts to make Google API calls
Batching requests
Caching results
Restricting return data

5 Managing Application Performance Monitoring

Managing Compute Engine VMs

Analyzing logs
Debugging a custom VM image using the serial port
Viewing syslogs from a VM
Analyzing a failed Compute Engine VM startup
Sending logs from a VM to Cloud Monitoring

Managing Google Kubernetes Engine workloads

Analyzing container lifecycle events (e.g., CrashLoopBackOff, ImagePullErr)
Analyzing logs
Configuring workload autoscaling
Using external metrics and corresponding alerts
Configuring logging and monitoring

Troubleshooting application performance

Viewing logs in the Google Cloud Console
Using Cloud Debugger
Using documentation, forums, and Google support
Profiling performance of request-response
Reviewing stack traces for error analysis
Creating a monitoring dashboard
Exporting logs from Google Cloud
Writing custom metrics and creating metrics from logs
Monitoring and profiling a running application
Reviewing application performance (e.g., Cloud Trace, Prometheus, OpenCensus)

Who should take the Google Professional Cloud Developer exam

Individuals should pursue the Google Professional Cloud Developer Exam if they want to demonstrate their expertise and ability to design highly scalable, available, and reliable cloud-native applications and deploy applications. It's perfect for solutions and/or enterprise architects, systems administrators or operations team members or simply any professional who wants in on this specific area of IT and cloud. A Professional Cloud Developer should have skills at producing meaningful metrics and logs to debug and trace code and proficiency with at least one general-purpose programming language.

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Google Professional-Cloud-Developer Exam Syllabus Topics:

Topic	Details
Topic 1	Google-Recommended Practices And Documentation Deploying And Securing An API With Cloud Endpoints
Topic 2	Designing Highly Scalable, Available, And Reliable Cloud-Native Applications Geographic Distribution Of Google Cloud Services
Topic 3	Automating Resource Provisioning With Deployment Manager Creating An Instance With A Startup Script That Installs Software
Topic 4	Developing An Integration Pipeline Using Services Emulating GCP Services For Local Application Development
Topic 5	Setting Up Your Development Environment, Considerations Building And Testing Applications
Topic 6	Launching A Compute Instance Using GCP Console And Cloud SDK Creating An Autoscaled Managed Instance Group Using An Instance Template
Topic 7	Defining A Key Structure For High Write Applications Using Cloud Storage Using Cloud Storage To Run A Static Website
Topic 8	Deploying Applications And Services On Google Kubernetes Engine Deploying Applications And Services On Google Kubernetes Engine
Topic 9	Implementing Appropriate Deployment Strategies Based On The Target Compute Environment Creating A Load Balancer For Compute Engine Instances
Topic 10	Configuring Compute Services Network Settings Configuring A Cloud Pub Sub Push Subscription To Call An Endpoint
Topic 11	Integrating An Application With Data And Storage Services Writing An SQL Query To Retrieve Data From Relational Databases
Topic 12	Publishing And Consuming From Data Ingestion Sources Authenticating Users By Using Oauth2 Web Flow And Identity Aware Proxy
Topic 13	Reading And Updating An Entity In A Cloud Datastore Transaction From An Application Using Apis To Read Write To Data Services
Topic 14	Operating System Versions And Base Runtimes Of Services Oogle-Recommended Practices And Documentation
Topic 15	Reviewing Test Results Of Continuous Integration Pipeline Developing Unit Tests For All Code Written
Topic 16	Eploying Applications And Services On Compute Engine Deploying An Application To App Engine
Topic 17	Security Mechanisms That Protect Services And Resources Choosing Data Storage Options Based On Use Case Considerations

Google Certified Professional - Cloud Developer Sample Questions (Q174-Q179):

NEW QUESTION # 174
Your company is planning to migrate their on-premises Hadoop environment to the cloud. Increasing storage cost and maintenance of data stored in HDFS is a major concern for your company. You also want to make minimal changes to existing data analytics jobs and existing architecture.
How should you proceed with the migration?

A. Create a Cloud Dataproc cluster on Google Cloud Platform, and then migrate your Hadoop environment to the new Cloud Dataproc cluster. Move your HDFS data into larger HDD disks to save on storage costs.
B. Migrate your data stored in Hadoop to BigQuery. Change your jobs to source their information from BigQuery instead of the on-premises Hadoop environment.
C. Create a Cloud Dataproc cluster on Google Cloud Platform, and then migrate your Hadoop code objects to the new cluster. Move your data to Cloud Storage and leverage the Cloud Dataproc connector to run jobs on that data.
D. Create Compute Engine instances with HDD instead of SSD to save costs. Then perform a full migration of your existing environment into the new one in Compute Engine instances.

Answer: C

NEW QUESTION # 175
Case study
This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided.
To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study.
At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section.
To start the case study
To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question.
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's .net-based auth service fails under intermittent load.
What should they do?

A. Use App Engine for autoscaling.
B. Use Cloud Functions for autoscaling.
C. Use a Compute Engine cluster for the service.
D. Use a dedicated Compute Engine virtual machine instance for the service.

Answer: D

Explanation:
Explanation/Reference: https://www.qwiklabs.com/focuses/611?parent=catalog

NEW QUESTION # 176
You are building a CI/CD pipeline that consists of a version control system, Cloud Build, and Container Registry. Each time a new tag is pushed to the repository, a Cloud Build job is triggered, which runs unit tests on the new code builds a new Docker container image, and pushes it into Container Registry. The last step of your pipeline should deploy the new container to your production Google Kubernetes Engine (GKE) cluster.
You need to select a tool and deployment strategy that meets the following requirements:
* Zero downtime is incurred
* Testing is fully automated
* Allows for testing before being rolled out to users
* Can quickly rollback if needed
What should you do?

A. Trigger a Spinnaker pipeline configured as an A/B test of your new code and, if it is successful, deploy the container to production.
B. Trigger another Cloud Build job that uses the Kubernetes CLI tools to deploy your new container to your GKE cluster, where you can perform a shadow test.
C. Trigger a Spinnaker pipeline configured as a canary test of your new code and, if it is successful, deploy the container to production.
D. Trigger another Cloud Build job that uses the Kubernetes CLI tools to deploy your new container to your GKE cluster, where you can perform a canary test.

Answer: B

Explanation:
Explanation
https://cloud.google.com/architecture/implementing-deployment-and-testing-strategies-on-gke#perform_a_shado With a shadow test, you test the new version of your application by mirroring user traffic from the current application version without impacting the user requests.

NEW QUESTION # 177
HipLocal wants to reduce the number of on-call engineers and eliminate manual scaling.
Which two services should they choose? (Choose two.)

A. Use a large Google Compute Engine cluster for deployments.
B. Use serverless Google Cloud Functions.
C. Use Google App Engine services.
D. Use Google Kubernetes Engine for automated deployments.
E. Use Knative to build and deploy serverless applications.

Answer: B,E

NEW QUESTION # 178
You are using Cloud Build to build and test application source code stored in Cloud Source Repositories. The build process requires a build tool not available in the Cloud Build environment.
What should you do?

A. Build a custom cloud builder image and reference the image in your build steps.
B. Download the binary from the internet during the build process.
C. Include the binary in your Cloud Source Repositories repository and reference it in your build scripts.
D. Ask to have the binary added to the Cloud Build environment by filing a feature request against the Cloud Build public Issue Tracker.

Answer: A

NEW QUESTION # 179
......

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