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

Google PROFESSIONAL-CLOUD-DEVELOPER Online Questions & Answers

• Question 1:

  You work for an organization that manages an ecommerce site. Your application is deployed behind an external Application Load Balancer. You need to test a new product recommendation algorithm. You plan to use A/B testing to determine the new algorithm's effect on sales in a randomized way.

  How should you test this feature?

  A. Split traffic between versions using weights.
  B. Enable the new recommendation feature flag on a single instance.
  C. Mirror traffic to the new version of your application.
  D. Use HTTP header-based routing.
  A. Split traffic between versions using weights.

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  Explanation

  A/B testing requires distributing traffic in a controlled, randomized manner between different versions of your application. Weighted traffic splitting allows you to direct a specific percentage of traffic to the new version (with the new algorithm) and the rest to the current version. This approach provides a statistically valid way to compare the two versions' performance and assess the impact on sales.

  HTTP header-based routing could target specific user segments but would not ensure random distribution for A/B testing. Mirroring traffic duplicates requests without user interactions being served by the new version, which doesn't provide direct test results. Enabling the feature on a single instance does not provide a randomized, scalable test approach.

• Question 2:

  You want to re-architect a monolithic application so that it follows a microservices model. You want to accomplish this efficiently while minimizing the impact of this change to the business.

  Which approach should you take?

  A. Deploy the application to Compute Engine and turn on autoscaling.
  B. Replace the application's features with appropriate microservices in phases.
  C. Refactor the monolithic application with appropriate microservices in a single effort and deploy it.
  D. Build a new application with the appropriate microservices separate from the monolith and replace it when it is complete.
  B. Replace the application's features with appropriate microservices in phases.

• Question 3:

  Which database should HipLocal use for storing user activity?

  A. BigQuery
  B. Cloud SQL
  C. Cloud Spanner
  D. Cloud Datastore
  A. BigQuery

• Question 4:

  You are designing a chat room application that will host multiple rooms and retain the message history for each room. You have selected Firestore as your database.

  How should you represent the data in Firestore?

  

  A. Option A
  B. Option B
  C. Option C
  D. Option D
  C. Option C

• Question 5:

  You work for a web development team at a small startup. Your team is developing a Node.js application using Google Cloud services, including Cloud Storage and Cloud Build. The team uses a Git repository for version control. Your manager calls you over the weekend and instructs you to make an emergency update to one of the company's websites, and you're the only developer available. You need to access Google Cloud to make the update, but you don't have your work laptop. You are not allowed to store source code locally on a non-corporate computer.

  How should you set up your developer environment?

  A. Use a text editor and the Git command line to send your source code updates as pull requests from a public computer.
  B. Use a text editor and the Git command line to send your source code updates as pull requests from a virtual machine running on a public computer.
  C. Use Cloud Shell and the built-in code editor for development. Send your source code updates as pull requests.
  D. Use a Cloud Storage bucket to store the source code that you need to edit. Mount the bucket to a public computer as a drive, and use a code editor to update the code. Turn on versioning for the bucket, and point it to the team's Git repository.
  C. Use Cloud Shell and the built-in code editor for development. Send your source code updates as pull requests.

• Question 6:

  You recently developed a new service on Cloud Run. The new service authenticates using a custom service and then writes transactional information to a Cloud Spanner database. You need to verify that your application can support up to 5,000 read and 1,000 write transactions per second while identifying any bottlenecks that occur. Your test infrastructure must be able to autoscale.

  What should you do?

  A. Build a test harness to generate requests and deploy it to Cloud Run. Analyze the VPC Flow Logs using Cloud Logging.
  B. Create a Google Kubernetes Engine cluster running the Locust or JMeter images to dynamically generate load tests. Analyze the results using Cloud Trace.
  C. Create a Cloud Task to generate a test load. Use Cloud Scheduler to run 60,000 Cloud Task transactions per minute for 10 minutes. Analyze the results using Cloud Monitoring.
  D. Create a Compute Engine instance that uses a LAMP stack image from the Marketplace, and use Apache Bench to generate load tests against the service. Analyze the results using Cloud Trace.
  B. Create a Google Kubernetes Engine cluster running the Locust or JMeter images to dynamically generate load tests. Analyze the results using Cloud Trace.

• Question 7:

  Your application performs well when tested locally, but it runs significantly slower after you deploy it to a Compute Engine instance. You need to diagnose the problem. What should you do?

  What should you do?

  A. File a ticket with Cloud Support indicating that the application performs faster locally.
  B. Use Cloud Debugger snapshots to look at a point-in-time execution of the application.
  C. Use Cloud Profiler to determine which functions within the application take the longest amount of time.
  D. Add logging commands to the application and use Cloud Logging to check where the latency problem occurs.
  C. Use Cloud Profiler to determine which functions within the application take the longest amount of time.

• Question 8:

  You are deploying a microservices application to GKE. One microservice needs to download files from a Cloud Storage bucket. You have an IAM service account with the Storage Object Viewer role on the project with the bucket. You need to configure your application to access the Cloud Storage bucket while following Google-recommended practices.

  What should you do?

  A. Assign the IAM service account to the cluster's node pool. Configure the application to authenticate to the bucket by using Application Default Credentials.
  B. Assign the IAM service account to the cluster's node pool. Encrypt the IAM service account key file by using a symmetric block cipher, and store the encrypted file on a persistent volume. Store the encryption key in Secret Manager.
  C. Create a Kubernetes service account. Create a Kubernetes secret with a base64-encoded IAM service account key file. Annotate the Kubernetes secret with the Kubernetes service account. Assign the Kubernetes ServiceAccount to the Pods that need to access the bucket.
  D. Create a Kubernetes service account. Use an IAM policy to bind the IAM service account to a Kubernetes service account. Annotate the Kubernetes ServiceAccount object with the name of the bound IAM service account. Assign the Kubernetes ServiceAccount to the Pods that need to access the bucket.
  D. Create a Kubernetes service account. Use an IAM policy to bind the IAM service account to a Kubernetes service account. Annotate the Kubernetes ServiceAccount object with the name of the bound IAM service account. Assign the Kubernetes ServiceAccount to the Pods that need to access the bucket.

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  Explanation

  This approach leverages Workload Identity, which is the Google-recommended way to securely allow applications running on GKE to access Google Cloud resources without needing to manage service account keys. By binding the IAM service account to a Kubernetes service account and annotating the Kubernetes service account with the IAM service account, the Pods can securely access Cloud Storage using Application Default Credentials.

  This method follows best practices by avoiding static service account keys and instead using the identity federation between Kubernetes and Google Cloud IAM, which is secure and eliminates the need for key management.

• Question 9:

  You need to containerize a web application that will be hosted on Google Cloud behind a global load balancer with SSL certificates. You don't have the time to develop authentication at the application level, and you want to offload SSL encryption and management from your application. You want to configure the architecture using managed services where possible.

  What should you do?

  A. Host the application on Google Kubernetes Engine, and deploy an NGINX Ingress Controller to handle authentication.
  B. Host the application on Google Kubernetes Engine, and deploy cert-manager to manage SSL certificates.
  C. Host the application on Compute Engine, and configure Cloud Endpoints for your application.
  D. Host the application on Google Kubernetes Engine, and use Identity-Aware Proxy (IAP) with Cloud Load Balancing and Google-managed certificates.
  D. Host the application on Google Kubernetes Engine, and use Identity-Aware Proxy (IAP) with Cloud Load Balancing and Google-managed certificates.

• Question 10:

  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. Migrate your data stored in Hadoop to BigQuery. Change your jobs to source their information from BigQuery instead of the on-premises Hadoop environment.
  B. 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.
  C. 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.
  D. 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 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.