GCP Developer Certification Dumps

/ Certification Exam / Certification Exam, Google Cloud Platform

Unlock your cloud career with the GCP Developer Certification. Learn about exam details, preparation strategies, and benefits of becoming a Google Professional Cloud Developer.

1. What is the GCP Developer Certification?

The Google Professional Cloud Developer certification validates your ability to design, build, test, and deploy scalable applications on GCP. Unlike associate-level certifications, this professional credential targets developers with hands-on experience in cloud-native solutions, APIs, and services like Cloud Run, Kubernetes Engine, and Pub/Sub.

2. Who Should Pursue This Certification?

  • Developers with 3+ years of experience, including 1+ year designing GCP solutions.
  • Professionals seeking to showcase expertise in modern application development, CI/CD pipelines, and microservices architecture.

3. Why Earn the Google Professional Cloud Developer Certification?

  1. Career Advancement: Stand out in job markets where demand for certified GCP developers surged by 35% in 2023 (Source: LinkedIn Talent Insights).
  2. Skill Validation: Prove mastery of GCP tools, including Cloud Functions, Firebase, and serverless computing.
  3. Higher Salary Potential: Certified professionals earn an average of $130,000 annually, 20% higher than non-certified peers (Payscale, 2024).
  4. Industry Recognition: Join a network of elite developers trusted by enterprises like Spotify, HSBC, and Twitter.

4. Exam Breakdown: What to Expect

  • Format: 50–60 multiple-choice and multiple-select questions.
  • Duration: 2 hours.
  • Cost: $200 (plus taxes).
  • Topics Covered:
    • Designing scalable applications using GCP services.
    • Securing applications with IAM, encryption, and VPCs.
    • Implementing CI/CD pipelines with Cloud Build and Spinnaker.
    • Monitoring and debugging using Cloud Monitoring and Logging.

Passing Score: Google does not publish exact figures, but experts estimate ~70%.

5. How to Prepare for the GCP Developer Certification

  1. Leverage Official Resources:
  2. Build Real-World Projects:
    • Deploy a serverless app with Cloud Functions.
    • Containerize an application using Kubernetes Engine.
  3. Practice with Mock Exams:
    • Use platforms to simulate test conditions.
  4. Join Study Communities:
    • Engage with peers on Reddit’s r/googlecloud or LinkedIn groups focused on GCP certifications.

6. Tips for Exam Day Success

  • Time Management: Allocate 1–2 minutes per question; flag tough ones for review.
  • Focus on Scenarios: Many questions present real-world problems requiring multi-step solutions.
  • Review GCP Best Practices: Understand cost optimization, security, and reliability principles.

7. Maintaining Your Certification

The Google Professional Cloud Developer credential is valid for two years. Renew by:

  1. Completing the recertification exam.
  2. Earning continuing education credits via Google Cloud events or training.
GCP Developer Certification

GCP Developer Certification Dumps

Q1. Your application performs well when tested locally, but it runs significantly slower when you deploy it to the App Engine standard environment. You want to diagnose the problem. What should you do?

  1. File a ticket with Cloud Support indicating that the application performs faster locally.
  2. Use Stackdriver Debugger Snapshots to look at a point-in-time execution of the application.
  3. Use Stackdriver Trace to determine which functions within the application have higher latency.✔️
  4. Add logging commands to the application and use Stackdriver Logging to check where the latency problem occurs.

Reference

Q2. You have an application running in App Engine. Your application is instrumented with Stackdriver Trace. The /product-details request reports details about four known unique products at /sku-details as shown below. You want to reduce the time it takes for the request to complete. What should you do?

  1. Increase the size of the instance class.
  2. Change the Persistent Disk type to SSD.
  3. Change /product-details to perform the requests in parallel.✔️
  4. Store the /sku-details information in a database, and replace the webservice call with a database query.

Reference

Q3. Your company has a data warehouse that keeps your application information in BigQuery. The BigQuery data warehouse keeps 2 PBs of user data. Recently, your company expanded your user base to include EU users and needs to comply with these requirements:
Your company must be able to delete all user account information upon user request.
All EU user data must be stored in a single region specifically for EU users.
Which two actions should you take?

  1. Use BigQuery federated queries to query data from Cloud Storage.
  2. Create a dataset in the EU region that will keep information about EU users only.✔️
  3. Create a Cloud Storage bucket in the EU region to store information for EU users only.
  4. Re-upload your data using a Cloud Dataflow pipeline by filtering your user records out.
  5. Use DML statements in BigQuery to update/delete user records based on their requests.✔️

Reference

Q4. Your App Engine standard configuration is as follows:
service: production
instance_class: B1
You want to limit the application to 5 instances. Which code snippet should you include in your configuration?

  1. manual_scaling: instances: 5 min_pending_latency: 30ms
  2. manual_scaling: max_instances: 5 idle_timeout: 10m
  3. basic_scaling: instances: 5 min_pending_latency: 30ms
  4. basic_scaling: max_instances: 5 idle_timeout: 10m✔️

Reference

Q5. Your analytics system executes queries against a BigQuery dataset. The SQL query is executed in batch and passes the contents of a SQL file to the BigQuery CLI. Then it redirects the BigQuery CLI output to another process. However, you are getting a permission error from the BigQuery CLI when the queries are executed. You want to resolve the issue. What should you do?

  1. Grant the service account BigQuery Data Viewer and BigQuery Job User roles.✔️
  2. Grant the service account BigQuery Data Editor and BigQuery Data Viewer roles.
  3. Create a view in BigQuery from the SQL query and SELECT* from the view in the CLI.
  4. Create a new dataset in BigQuery, and copy the source table to the new dataset Query the new dataset and table from the CLI.

Reference

Q6. 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?

  1. Reboot the machine.
  2. Enable and check the serial port output.✔️
  3. Delete the machine and create a new one.
  4. Take a snapshot of the disk and attach it to a new machine.

Reference

Q7. You configured your Compute Engine instance group to scale automatically according to overall CPU usage. However, your application’s response latency increases sharply before the cluster has finished adding up instances. You want to provide a more consistent latency experience for your end users by changing the configuration of the instance group autoscaler. Which two configuration changes should you make?

  1. Add the label “AUTOSCALE” to the instance group template.
  2. Decrease the cool-down period for instances added to the group.✔️
  3. Increase the target CPU usage for the instance group autoscaler.
  4. Decrease the target CPU usage for the instance group autoscaler.✔️
  5. Remove the health check for individual VMs in the instance group.

Reference

Q8. You have an application controlled by a managed instance group. When you deploy a new version of the application, costs should be minimized, and the number of instances should not increase. You want to ensure that, when each new instance is created, the deployment only continues if the new instance is healthy. What should you do?

  1. Perform a rolling action with maxSurge set to 1, maxUnavailable set to 0.
  2. Perform a rolling action with maxSurge set to 0, maxUnavailable set to 1✔️
  3. Perform a rolling action with maxHealthy set to 1, maxUnhealthy set to 0.
  4. Perform a rolling action with maxHealthy set to 0, maxUnhealthy set to 1.

Reference

Q9.Your application requires service accounts to be authenticated to GCP products via credentials stored on its host Compute Engine virtual machine instances. You want to distribute these credentials to the host instances as securely as possible. What should you do?

  1. Use HTTP signed URLs to securely provide access to the required resources.
  2. Use the instance’s service account Application Default Credentials to authenticate to the required resources.✔️
  3. Generate a P12 file from the GCP Console after the instance is deployed, and copy the credentials to the host instance before starting the application.
  4. Commit the credential JSON file into your application’s source repository, and have your CI/CD process package it with the software that is deployed to the instance.

Reference

Q10. Your application is deployed in a Google Kubernetes Engine (GKE) cluster. You want to expose this application publicly behind a Cloud Load Balancing HTTP(S) load balancer. What should you do?

  1. Configure a GKE Ingress resource.✔️
  2. Configure a GKE Service resource.
  3. Configure a GKE Ingress resource with type: LoadBalancer.
  4. Configure a GKE Service resource with type: LoadBalancer.

Reference

Q11. Your company is planning to migrate its on-premises Hadoop environment to the cloud. Increasing storage costs and maintenance of data stored in HDFS are 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?

  1. Migrate your data stored in Hadoop to BigQuery. Change your jobs to source their information from BigQuery instead of the on-premises Hadoop environment.
  2. 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.
  3. 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.
  4. 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.✔️

Reference

Q12. Your data is stored in Cloud Storage buckets. Fellow developers have reported that data downloaded from Cloud Storage is resulting in slow API performance. You want to research the issue to provide details to the GCP support team. Which command should you run?

  1. gsutil test -o output.json gs://my-bucket
  2. gsutil perfdiag -o output.json gs://my-bucket✔️
  3. gcloud compute scp example-instance:~/test-data -o output.json gs://mybucket
  4. gcloud services test -o output.json gs://my-bucket

Reference

Q13. You are using Cloud Build to promote a Docker image to Development, Test, and Production environments. You need to ensure that the same Docker image is deployed to each of these environments. How should you identify the Docker image in your build?

  1. Use the latest Docker image tag.
  2. Use a unique Docker image name.
  3. Use the digest of the Docker image.
  4. Use a semantic version Docker image tag.✔️

Reference

Q14. Your company has created an application that uploads a report to a Cloud Storage bucket. When the report is uploaded to the bucket, you want to publish a message to a Cloud Pub/Sub topic. You want to implement a solution that will take a small amount to effort to implement. What should you do?

  1. Configure the Cloud Storage bucket to trigger Cloud Pub/Sub notifications when objects are modified.✔️
  2. Create an App Engine application to receive the file; when it is received, publish a message to the Cloud Pub/Sub topic.
  3. Create a Cloud Function that is triggered by the Cloud Storage bucket. In the Cloud Function, publish a message to the Cloud Pub/Sub topic.
  4. Create an application deployed in a Google Kubernetes Engine cluster to receive the file; when it is received, publish a message to the Cloud Pub/Sub topic.

Reference

Q15. Your teammate has asked you to review the code below, which is adding a credit to an account balance in Cloud Datastore. Which improvement should you suggest your teammate make?

  1. Get the entity with an ancestor query.
  2. Get and put the entity in a transaction.✔️
  3. Use a strongly consistent transactional database.
  4. Don’t return the account entity from the function.

Reference

Q16. Your company stores its source code in a Cloud Source Repositories repository. Your company wants to build and test their code on each source code commit to the repository and requires a solution that is managed and has minimal operational overhead. Which method should they use?

  1. Use Cloud Build with a trigger configured for each source code commit.✔️
  2. Use Jenkins deployed via the Google Cloud Platform Marketplace, configured to watch for source code commits.
  3. Use a Compute Engine virtual machine instance with an open-source continuous integration tool, configured to watch for source code commits.
  4. Use a source code commit trigger to push a message to a Cloud Pub/Sub topic that triggers an App Engine service to build the source code.

Reference

Q17. You are writing a Compute Engine-hosted application in project A that needs to securely authenticate to a Cloud Pub/Sub topic in project B. What should you do?

  1. Configure the instances with a service account owned by project B. Add the service account as a Cloud Pub/Sub publisher to project A.
  2. Configure the instances with a service account owned by project A. Add the service account as a publisher on the topic.✔️
  3. Configure Application Default Credentials to use the private key of a service account owned by project B. Add the service account as a Cloud Pub/Sub publisher to project A.
  4. Configure Application Default Credentials to use the private key of a service account owned by project A. Add the service account as a publisher on the topic

Reference

Q18. You are developing a corporate tool on Compute Engine for the finance department, which needs to authenticate users and verify that they are in the finance department. All company employees use G Suite. What should you do?

  1. Enable Cloud Identity-Aware Proxy on the HTTP(s) load balancer and restrict access to a Google Group containing users in the finance department. Verify the provided JSON Web Token within the application.✔️
  2. Enable Cloud Identity-Aware Proxy on the HTTP(s) load balancer and restrict access to a Google Group containing users in the finance department. Issue client-side certificates to everybody in the finance team and verify the certificates in the application.
  3. Configure Cloud Armor Security Policies to restrict access to only corporate IP address ranges. Verify the provided JSON Web Token within the application.
  4. Configure Cloud Armor Security Policies to restrict access to only corporate IP address ranges. Issue client-side certificates to everybody in the finance team and verify the certificates in the application.

Reference

Q19. Your API backend is running on multiple cloud providers. You want to generate reports for the network latency of your API. Which two steps should you take?

  1. Use the Zipkin collector to gather data.✔️
  2. Use Fluentd agent to gather data.
  3. Use Stackdriver Trace to generate reports.✔️
  4. Use Stackdriver Debugger to generate the report.
  5. Use Stackdriver Profiler to generate the report.

Reference

Q20. For this Question, please refer to the HipLocal case study: https://services.google.com/fh/files/blogs/master_case_study_hiplocal.pdf
Which database should HipLocal use for storing user activity?

  1. BigQuery✔️
  2. Cloud SQL
  3. Cloud Spanner
  4. Cloud Datastore

Reference

Q21. For this Question, please refer to the HipLocal case study: https://services.google.com/fh/files/blogs/master_case_study_hiplocal.pdf
HipLocal is configuring its access controls. Which firewall configuration should they implement?

  1. Block all traffic on port 443.
  2. Allow all traffic into the network.
  3. Allow traffic on port 443 for a specific tag.✔️
  4. Allow all traffic on port 443 into the network.

Reference

Q22. For this Question, please refer to the HipLocal case study: https://services.google.com/fh/files/blogs/master_case_study_hiplocal.pdf
HipLocal’s data science team wants to analyze user reviews. How should they prepare the data?

  1. Use the Cloud Data Loss Prevention API for redaction of the review dataset.
  2. Use the Cloud Data Loss Prevention API for de-identification of the review dataset.✔️
  3. Use the Cloud Natural Language Processing API for redaction of the review dataset.
  4. Use the Cloud Natural Language Processing API for de-identification of the review dataset.

Reference

Q23. For this Question, please refer to the HipLocal case study: https://services.google.com/fh/files/blogs/master_case_study_hiplocal.pdf
For HipLocal to store application state and meet their stated business requirements, which database service should they migrate to?

  1. Cloud Spanner✔️
  2. Cloud Datastore
  3. Cloud Memorystore as a cache
  4. Separate Cloud SQL clusters for each region

Reference

Q24. You have an application deployed in production. When a new version is deployed, you want to ensure that all production traffic is routed to the new version of your application. You also want to keep the previous version deployed so that you can revert to it if there is an issue with the new version. Which deployment strategy should you use?

  1. Blue/green deployment✔️
  2. Canary deployment
  3. Rolling deployment
  4. Recreate deployment

Reference

Q25. You are running an application on Compute Engine and collecting logs through Stackdriver. You discover that some personally identifiable information (PII) is leaking into certain log entry fields. All the Pll entries begin with the text userinfo. You want to capture these log entries in a secure location for later review and prevent them from leaking to Stackdriver Logging. What should you do?

  1. Create a basic log filter matching userinfo, and then configure a log export in the Stackdriver console with Cloud Storage as a sink.
  2. Use a Fluentd filter plugin with the Stackdriver Agent to remove log entries containing userinfo and then copy the entries to a Cloud Storage bucket.✔️
  3. Create an advanced log filter matching userinfo, configure a log export in the Stackdriver console with Cloud Storage as a sink, and then configure a tog exclusion with userinfo as a filter.
  4. Use a Fluentd filter plugin with the Stackdriver Agent to remove log entries containing userinfo, create an advanced log filter matching userinfo, and then configure a log export in the Stackdriver console with Cloud Storage as a sink.

Reference

Q26. Your product is currently deployed in three Google Cloud Platform (GCP) zones, with your users divided between the zones. You can failover from one zone to another, but it causes a 10-minute service disruption for the affected users. You typically experience a database failure once per quarter and can detect it within five minutes. You are cataloging the reliability risks of a new real-time chat feature for your product. You catalog the following information for each risk. The chat feature requires a new database system that takes twice as long to successfully fail over between zones. You want to account for the risk of the new database failing in one zone. What would be the values for the risk of database failover with the new system?

  1. MTTD: 5
    MTTR: 10
    MTBF: 90
    Impact: 33%✔️
  2. MTTD:5
    MTTR: 20
    MTBF: 90
    Impact: 33%
  3. MTTD:5
    MTTR: 10
    MTBF: 90
    Impact 50%
  4. MTTD:5
    MTTR: 20
    MTBF: 90
    Impact: 50%

Reference

Q27. Your company experiences bugs, outages, and slowness in its production systems. Developers use the production environment for new feature development and bug fixes. Configuration and experiments are done in the production environment, causing outages for users. Testers use the production environment for load testing, which often slows the production systems. You need to redesign the environment to reduce the number of bugs and outages in production and to enable testers to load test new features. What should you do?

  1. Create an automated testing script in production to detect failures as soon as they occur.
  2. Create a development environment with a smaller server capacity and give access only to developers and testers.
  3. Secure the production environment to ensure that developers can’t change it and set up one controlled update per year.
  4. Create a development environment for writing code and a test environment for configurations, experiments, and load testing.✔️

Reference

Q28. Your team has recently deployed an NGINX-based application into Google Kubernetes Engine (GKE) and has exposed it to the public via an HTTP Google Cloud Load Balancer (GCLB) ingress. You want to scale the deployment of the application’s frontend using an appropriate Service Level Indicator (SLI). What should you do?

  1. Configure the horizontal pod autoscaler to use the average response time from the Liveness and Readiness Probes.
  2. Configure the vertical pod autoscaler in GKE and enable the cluster autoscaler to scale the cluster as pods expand.
  3. Horizontal pod autoscaler to use the number of requests provided by the GCLB.✔️
  4. Expose the NGINX stats endpoint and configure the horizontal pod autoscaler to use the request metrics exposed by the NGINX deployment.

Reference

Q29. You support the backend of a mobile phone game that runs on a Google Kubernetes Engine (GKE) cluster. The application is serving HTTP requests from users. You need to implement a solution that will reduce the network cost. What should you do?

  1. Configure the VPC as a Shared VPC Host project.
  2. Configure your network services on the Standard Tier.✔️
  3. Configure your Kubernetes cluster as a Private Cluster.
  4. Configure a Google Cloud HTTP Load Balancer as an Ingress.

Reference

Q30. You manage an application that writes logs to Stackdriver Logging. You need to give some team members the ability to export logs. What should you do?

  1. Grant the team members the IAM role of logging.configWriter on Cloud IAM.✔️
  2. Configure Access Context Manager to allow only these members to export logs.
  3. Create and grant a custom IAM role with the permissions logging.sinks.list and logging.sink.get.
  4. Create an Organizational Policy in Cloud IAM to allow only these members to create log exports.

Reference

Q31. You have written a Cloud Function in Node.js with source code stored in a Git repository. You want any committed changes to the source to be automatically tested. You write a Cloud Build configuration that pushes the source to a uniquely named Cloud Function, then calls the function as a test, and then deletes the Cloud Function as cleanup. You discover that if the test fails, the Cloud Function is not deleted. What should you do?

  1. Change the order of the steps to delete the Cloud Function before performing the test, which can indicate a failure.
  2. Include a waitFor option in the configuration for the Cloud Function deletion that identifies the test step as a required preceding step.
  3. Have the test write its results to a file and return 0. Add a final step after the Cloud Function deletion that checks whether the file contains the expected results.✔️
  4. Have the test set its outcome in an environment variable called result and return 0. Add a final step after the Cloud Function deletion that checks whether the result contains the expected results.

Reference

Q32. For this Question, please refer to the HipLocal case study: https://services.google.com/fh/files/blogs/master_case_study_hiplocal.pdf
Which database should HipLocal use for storing state while minimizing application changes?

  1. Firestore
  2. BigQuery
  3. Cloud SQL✔️
  4. Cloud Bigtable

Reference

Q33. For this Question, please refer to the HipLocal case study: https://services.google.com/fh/files/blogs/master_case_study_hiplocal.pdf
HipLocal needs to implement a solution that meets their business requirement of supporting a large increase in concurrent users. What approach should they take?

  1. Use a load testing tool to load-test the application.✔️
  2. Use a code test coverage tool to verify the code coverage.
  3. Use a security analysis tool to perform static security code analysis.
  4. Use a CI/CD tool to introduce canary testing into the release pipeline.

Reference

Q34. For this Question, please refer to the HipLocal case study: https://services.google.com/fh/files/blogs/master_case_study_hiplocal.pdf
Which architecture should HipLocal use for log analysis?

  1. Use Cloud Spanner to store each event.
  2. Start storing key metrics in Memorystore.
  3. Use Cloud Logging with a BigQuery sink.✔️
  4. Use Cloud Logging with a Cloud Storage sink.

Reference

Q35. Your company plans to expand its analytics use cases. One of the new use cases requires your data analysts to analyze events using SQL on a near–real–time basis. You expect rapid growth and want to use managed services as much as possible. What should you do?

  1. Create a Pub/Sub topic and a subscription. Stream your events from the source into the Pub/Sub topic. Leverage Dataflow to ingest these events into BigQuery.✔️
  2. Create a Pub/Sub topic and a subscription. Stream your events from the source into the Pub/Sub topic. Leverage Dataflow to ingest these events into Cloud Storage.
  3. Create a Pub/Sub topic and a subscription. Stream your events from the source into the Pub/Sub topic. Leverage Dataflow to ingest these events into Firestore in Datastore mode.
  4. Create a Kafka instance on a large Compute Engine instance. Stream your events from the source into a Kafka pipeline. Leverage Dataflow to ingest these events into Cloud Storage.

Q36. You are building a storage layer for an analytics Hadoop cluster in a region for your company. This cluster will run multiple jobs on a nightly basis, and you need to access the data frequently. You want to use Cloud Storage for this purpose. What is the most cost-effective option?

  1. Regional Coldline storage
  2. Regional Nearline storage
  3. Regional Standard storage✔️
  4. Multi-regional Standard storage

Reference

Q37. You have deployed your website in a managed instance group. The managed instance group is configured to have a size of three instances and to perform an HTTP health check on port 80. When the managed instance group is created, three instances are created and started. When you connect to the instance using SSH, you confirm that the website is running and available on port 80. However, the managed instance group is re-creating the instances when they fail verification. What should you do?

  1. Change the type to an unmanaged instance group.
  2. Disable autoscaling on the managed instance group.
  3. Increase the initial delay timeout to ensure that the instance is created.
  4. Check the firewall rules and ensure that the probes can access the instance.✔️

Reference

Q38. Your application starts on the virtual machine (VM) as a systemd service. Your application outputs its log information to stdout. You want to review the application logs. What should you do?

  1. Review the application logs from the Compute Engine VM instance activity logs in Cloud Logging.
  2. Review the application logs from the Compute Engine VM instance data access logs in Cloud Logging.
  3. Install Cloud Logging Agent. Review the application logs from the Compute Engine VM instance syslog logs in Cloud Logging.✔️
  4. Install Cloud Logging Agent. Review the application logs from the Compute Engine VM instance system event logs in Cloud Logging.

Reference

Q39. You create a Deployment with 2 replicas in a Google Kubernetes Engine cluster that has a single preemptible node pool. After a few minutes, you use kubectl to examine the status of your Pod and observe that one of them is still in Pending status. What is the most likely cause?

  1. The pending Pod’s resource requests are too large to fit on a single node of the cluster.
  2. Too many Pods are already running in the cluster, and there are not enough resources left to schedule the pending Pod.
  3. The node pool is configured with a service account that does not have permission to pull the container image used by the pending Pod.
  4. The pending Pod was originally scheduled on a node that has been preempted between the creation of the Deployment and your verification of the Pods’ status. It is currently being rescheduled on a new node.✔️

Reference

Q40. Your company processes high volumes of IoT data that are time-stamped. The total data volume can be several petabytes. The data needs to be written and changed at a high speed. You want to use the most performant storage option for your data. Which product should you use?

  1. Cloud Datastore
  2. Cloud Storage
  3. Cloud Bigtable✔️
  4. BigQuery

Reference

Related Posts

Scroll to Top