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? (Choose two.)

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

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Your company's development teams want to use Cloud Build in their projects to build and push Docker images to Container Registry. The operations team requires all Docker images to be published to a centralized, securely managed Docker registry that the operations team manages.
What should you do?

• A.Create a separate project for the operations team that has Container Registry configured. Create a Service Account for each development team and assign the appropriate permissions to allow it access to the operations team's registry. Store the service account key file in the source code repository and use it to authenticate against the operations team's registry.
• B.Create a separate project for the operations team that has the open source Docker Registry deployed on a Compute Engine virtual machine instance. Create a username and password for each development team.Store the username and password in the source code repository and use it to authenticate against the operations team's Docker registry.
• C.Use Container Registry to create a registry in each development team's project. Configure the Cloud Build build to push the Docker image to the project's registry. Grant the operations team access to each development team's registry.
• D.Create a separate project for the operations team that has Container Registry configured. Assign appropriate permissions to the Cloud Build service account in each developer team's project to allow access to the operation team's registry.

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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.Create a collection for the rooms. For each room, create a document that lists the contents of the messages
  
• B.Create a collection for the rooms. For each room, create a collection that contains a document for each message
  
• C.Create a collection for the rooms. For each room, create a document that contains a collection for documents, each of which contains a message.
  
• D.Create a collection for the rooms, and create a document for each room. Create a separate collection for messages, with one document per message. Each room's document contains a list of references to the messages.
  

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Case Study 1 - HipLocal
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.
Which database should HipLocal use for storing user activity?

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

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You have a GKE cluster that has three TPU nodes. You are running an ML training job on the cluster, and you observe log entries in Cloud Logging similar to the one below. To identify the root cause of a performance issue, you need to view all stdoutlogs from the containers running on the TPU nodes. What should you do?

• A.Run the following log query in Cloud Logging:
  
• B.Run the following log query in Cloud Logging:
  
• C.Run the following command in Cloud Shell:
  
• D.Run the following command in Cloud Shell:
  

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