Google PROFESSIONAL-CLOUD-DEVELOPER Online Practice Questions and Exam Preparation

Google PROFESSIONAL-CLOUD-DEVELOPER Online Questions & Answers

• Question 211:

  You are developing a new image processing application that needs to handle various tasks, such as resizing, cropping, and watermarking images. You also need to monitor the workflow and ensure that it scales efficiently when there are large volumes of images. You want to automate the image processing tasks and workflow monitoring with the least effort.

  What should you do?

  A. Employ Cloud Composer to manage the image processing workflows. Use Dataproc for workflow monitoring and analytics.
  B. Use Cloud Run to deploy the image processing functions. Use Apigee to expose the API. Use Cloud Logging for workflow monitoring.
  C. Implement Workflows to orchestrate the image processing tasks. Use Cloud Logging for workflow monitoring.
  D. Use Cloud Build to trigger Cloud Run functions for the image processing tasks. Use Cloud Monitoring for workflow monitoring.
  C. Implement Workflows to orchestrate the image processing tasks. Use Cloud Logging for workflow monitoring.

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  Explanation

  https://cloudinary.com/blog/mediaflows-automate-image-processing

• Question 212:

  Your team developed a web-based game that has many simultaneous players. Recently, users have started to complain that the leaderboard tallies the top scores too slowly. You investigated the issue and discovered that the application stack is currently using Cloud SQL for PostgreSQL. You want to improve the leaderboard performance as much as possible to provide a better user experience.

  What should you do?

  A. Re-implement the leaderboard data to be stored in Memorystore for Redis.
  B. Optimize the SQL queries to minimize slow-running queries.
  C. Migrate the database and store the data in AlloyDB.
  D. Update Cloud SQL for PostgreSQL to the latest version.
  A. Re-implement the leaderboard data to be stored in Memorystore for Redis.

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  Explanation

  Memorystore for Redis is an in-memory data store that provides sub-millisecond latency and is ideal for real-time leaderboard use cases, significantly improving performance for rapidly changing, high-read workloads compared to Cloud SQL.

• Question 213:

  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?

  A. Blue/green deployment
  B. Canary deployment
  C. Rolling deployment
  D. Recreate deployment
  A. Blue/green deployment

• Question 214:

  You are deploying a containerized application to GKE. You have set up a build pipeline by using Cloud Build that builds a Java application and pushes the application container image to Artifact Registry. Your build pipeline executes multiple sequential steps that reference Docker container images with the same layers.

  You notice that the Cloud Build pipeline runs are taking longer than expected to complete.

  How should you optimize the Docker image build process?

  A. Add the --squash parameter to the Docker build steps to combine newly built layers into a single layer.
  B. Configure Cloud Build to use a private pool in your VPC for pipeline executions.
  C. Specify the cached image by adding the --cache-from argument in your build config file with the image as a cache source.
  D. Store container artifacts on Cloud Storage. Configure Cloud CDN on the Cloud Storage bucket to enable caching on edge locations.
  C. Specify the cached image by adding the --cache-from argument in your build config file with the image as a cache source.

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  Explanation

  Using the --cache-from argument enables Docker layer caching by specifying a previous image as a cache source. This allows Cloud Build to reuse layers from a previously built image, significantly reducing build time, especially when layers have not changed between builds. This approach is effective in optimizing Docker image builds and is a recommended best practice for reducing build times in Cloud Build.

  The --squash parameter is no longer supported in Docker and does not directly improve build speed. Using private pools in Cloud Build or Cloud CDN for container artifacts does not directly address build efficiency and caching in Docker builds.

• Question 215:

  You are designing a schema for a table that will be moved from MySQL to Cloud Bigtable. The MySQL table is as follows:

  

  How should you design a row key for Cloud Bigtable for this table?

  A. Set Account_id as a key.
  B. Set Account_id_Event_timestamp as a key.
  C. Set Event_timestamp_Account_id as a key.
  D. Set Event_timestamp as a key.
  B. Set Account_id_Event_timestamp as a key.

• Question 216:

  Before promoting your new application code to production, you want to conduct testing across a variety of different users. Although this plan is risky, you want to test the new version of the application with production users and you want to control which users are forwarded to the new version of the application based on their operating system. If bugs are discovered in the new version, you want to roll back the newly deployed version of the application as quickly as possible.

  What should you do?

  A. Deploy your application on Cloud Run. Use traffic splitting to direct a subset of user traffic to the new version based on the revision tag.
  B. Deploy your application on Google Kubernetes Engine with Anthos Service Mesh. Use traffic splitting to direct a subset of user traffic to the new version based on the user-agent header.
  C. Deploy your application on App Engine. Use traffic splitting to direct a subset of user traffic to the new version based on the IP address.
  D. Deploy your application on Compute Engine. Use Traffic Director to direct a subset of user traffic to the new version based on predefined weights.
  B. Deploy your application on Google Kubernetes Engine with Anthos Service Mesh. Use traffic splitting to direct a subset of user traffic to the new version based on the user-agent header.

• Question 217:

  HipLocal wants to reduce the number of on-call engineers and eliminate manual scaling.

  Which two services should they choose? (Choose two.)

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

• Question 218:

  You want to upload files from an on-premises virtual machine to Google Cloud Storage as part of a data migration. These files will be consumed by Cloud DataProc Hadoop cluster in a GCP environment.

  Which command should you use?

  A. gsutil cp [LOCAL_OBJECT] gs://[DESTINATION_BUCKET_NAME]/
  B. gcloud cp [LOCAL_OBJECT] gs://[DESTINATION_BUCKET_NAME]/
  C. hadoop fs cp [LOCAL_OBJECT] gs://[DESTINATION_BUCKET_NAME]/
  D. gcloud dataproc cp [LOCAL_OBJECT] gs://[DESTINATION_BUCKET_NAME]/
  A. gsutil cp [LOCAL_OBJECT] gs://[DESTINATION_BUCKET_NAME]/

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  Explanation

  The gsutil cp command allows you to copy data between your local file. storage. boto files generated by running "gsutil config"

• Question 219:

  You are developing an application that will handle requests from end users. You need to secure a Cloud Function called by the application to allow authorized end users to authenticate to the function via the application while restricting access to unauthorized users. You will integrate Google Sign-In as part of the solution and want to follow Google-recommended best practices.

  What should you do?

  A. Deploy from a source code repository and grant users the roles/cloudfunctions.viewer role.
  B. Deploy from a source code repository and grant users the roles/cloudfunctions.invoker role
  C. Deploy from your local machine using gcloud and grant users the roles/cloudfunctions.admin role
  D. Deploy from your local machine using gcloud and grant users the roles/rolecloudfunctions.developer
  B. Deploy from a source code repository and grant users the roles/cloudfunctions.invoker role

• Question 220:

  You are a lead developer working on a new retail system that runs on Cloud Run and Firestore in Datastore mode. A web UI requirement is for the system to display a list of available products when users access the system and for the user to be able to browse through all products. You have implemented this requirement in the minimum viable product (MVP) phase by returning a list of all available products stored in Firestore.

  A few months after go-live, you notice that Cloud Run instances are terminated with HTTP 500:errors during busy times. This error Container instances are exceeding memory limits coincides with spikes in the number of Datastore entity reads.

  You need to prevent Cloud Run from crashing and decrease the number of Datastore entity reads. You want to use a solution that optimizes system performance.

  What should you do?

  A. Modify the query that returns the product list using integer offsets.
  B. Modify the query that returns the product list using limits.
  C. Modify the Cloud Run configuration to increase the memory limits.
  D. Modify the query that returns the product list using cursors.
  D. Modify the query that returns the product list using cursors.