Google Professional Cloud Developer Exam Question and Updated Info

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Exam Name: Professional Cloud Developer

Exam Code: Professional Cloud Developer

Related Certification(s): Google Cloud Certified Certification

Certification Provider: Google

Number of Professional Cloud Developer practice questions in our database: 265 (updated: Jan. 09, 2025)

Expected Professional Cloud Developer Exam Topics, as suggested by Google :

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

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A.Enforce signed URLs for the desired bucket. Grant the Storage Object Viewer IAM role on the bucket to the Compute Engine default service account.

B. Enforce public access prevention for the desired bucket. Grant the Storage Object Viewer IAM role on the bucket to the Compute Engine default service account.

C. Enforce signed URLs for the desired bucket Create and update the Cloud Run service to use a user -managed service account. Grant the Storage Object Viewer IAM role on the bucket to the service account

D. Enforce public access prevention for the desired bucket. Create and update the Cloud Run service to use a user-managed service account. Grant the Storage Object Viewer IAM role on the bucket to the service account.

A.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.

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. 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.

Q3. 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 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.

B. Each developer commits their code to the main branch before each product release, conducts testing, and rolls back if integration issues are detected.

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 after peer review.

A.Create an image that has cURL installed and configure cURLto run a test plan Deploy the image in a managed instance group, and run one instance of the image for each VM.

B. Create an image that has cURL installed and configure cURL to run a test plan Deploy the image in an unmanaged instance group, and run one instance of the image for each VM.

C. Deploy a distributed load testing framework on a private Google Kubernetes Engine Cluster Deploy additional Pods as needed to initiate more traffic and support the number of concurrent users.

D. Download the container image of a distributed load testing framework on Cloud Shell Sequentially start several instances of the container on Cloud Shell to increase the load on the API.

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