You have an application that uses Cloud Spanner as a database backend to keep current state information about users. Cloud Bigtable logs all events triggered by users. You export Cloud Spanner data to Cloud Storage during daily backups. One of your analysts asks you to join data from Cloud Spanner and Cloud Bigtable for specific users. You want to complete this ad hoc request as efficiently as possible. What should you do?

• A.Create a Cloud Dataproc cluster that runs a Spark job to extract data from Cloud Bigtable and Cloud Storage for specific users.
• B.Create two separate BigQuery external tables on Cloud Storage and Cloud Bigtable. Use the BigQuery console to join these tables through user fields, and apply appropriate filters.
• C.Create a dataflow job that copies data from Cloud Bigtable and Cloud Storage for specific users.
• D.Create a dataflow job that copies data from Cloud Bigtable and Cloud Spanner for specific users.

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You are creating an application that will run on Google Kubernetes Engine. You have identified MongoDB as the most suitable database system for your application and want to deploy a managed MongoDB environment that provides a support SLA. What should you do?

• A.Deploy MongoDB Alias from the Google Cloud Marketplace
• B.Download a MongoDB installation package and run it on Compute Engine instances
• C.Create a Cloud Bigtable cluster and use the HBase API
• D.Download a MongoDB installation package, and run it on a Managed Instance Group

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You want to run a single caching HTTP reverse proxy on GCP for a latency-sensitive website.
This specific reverse proxy consumes almost no CPU. You want to have a 30-GB in-memory cache, and need an additional 2 GB of memory for the rest of the processes. You want to minimize cost. How should you run this reverse proxy?

• A.Create a Cloud Memorystore for Redis instance with 32-GB capacity.
• B.Run it on Compute Engine, and choose a custom instance type with 6 vCPUs and 32 GB of memory.
• C.Package it in a container image, and run it on Kubernetes Engine, using n1-standard-32 instances as nodes.
• D.Run it on Compute Engine, choose the instance type n1-standard-1, and add an SSD persistent disk of 32 GB.

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The core business of your company is to rent out construction equipment at a large scale. All the equipment that is being rented out has been equipped with multiple sensors that send event information every few seconds. These signals can vary from engine status, distance traveled, fuel level, and more. Customers are billed based on the consumption monitored by these sensors. You expect high throughput - up to thousands of events per hour per device - and need to retrieve consistent data based on the time of the event. Storing and retrieving individual signals should be atomic. What should you do?

• A.Ingest the data into Cloud Bigtable. Create a row key based on the event timestamp.
• B.Ingest the data into Datastore. Store data in an entity group based on the device.
• C.Create a file in Cloud Storage per device and append new data to that file.
• D.Create a file in Cloud Filestore per device and append new data to that file.

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You will have several applications running on different Compute Engine instances in the same project. You want to specify at a more granular level the service account each instance uses when calling Google Cloud APIs. What should you do?

• A.When creating the instances, specify a Service Account for each instance
• B.When creating the instances, assign the name of each Service Account as instance metadata
• C.After starting the instances, use gcloud compute instances update to specify a Service Account for each instance
• D.After starting the instances, use gcloud compute instances update to assign the name of the relevant Service Account as instance metadata

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