For this question, refer to the TerramEarth case study. Considering the technical requirements, how should
you reduce the unplanned vehicle downtime in GCP?

• A.Use Cloud Dataproc Hive as the data warehouse. Directly stream data into prtitioned Hive tables. Use
  Pig scripts to analyze data.
• B.Use Cloud Dataproc Hive as the data warehouse. Upload gzip files to a MultiRegional Cloud Storage
  bucket. Upload this data into BigQuery using gcloud. Use Google data Studio for analysis and
  reporting.
• C.Use BigQuery as the data warehouse. Connect all vehicles to the network and stream data into
  BigQuery using Cloud Pub/Sub and Cloud Dataflow. Use Google Data Studio for analysis and
  reporting.
• D.Use BigQuery as the data warehouse. Connect all vehicles to the network and upload gzip files to a
  Multi-Regional Cloud Storage bucket using gcloud. Use Google Data Studio for analysis and reporting.

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You are developing your microservices application on Google Kubernetes Engine. During testing, you want to validate the behavior of your application in case a specific microservice should suddenly crash. What should you do?

• A.Add a taint to one of the nodes of the Kubernetes cluster. For the specific microservice, configure a pod anti-affinity label that has the name of the tainted node as a value.
• B.Use Istio's fault injection on the particular microservice whose faulty behavior you want to simulate.
• C.Destroy one of the nodes of the Kubernetes cluster to observe the behavior.
• D.Configure Istio's traffic management features to steer the traffic away from a crashing microservice.

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Your company captures all web traffic data in Google Analytics 360 and stores it in BigQuery. Each country has its own dataset. Each dataset has multiple tables. You want analysts from each country to be able to see and query only the data for their respective countries.
How should you configure the access rights?

• A.Create a group per country. Add analysts to their respective country-groups. Create a single group
  'all_analysts', and add all country-groups as members. Grant the 'all-analysts' group the IAM role of BigQuery jobUser. Share the appropriate dataset with view access with each respective analyst country- group.
• B.Create a group per country. Add analysts to their respective country-groups. Create a single group
  'all_analysts', and add all country-groups as members. Grant the 'all-analysts' group the IAM role of BigQuery jobUser. Share the appropriate tables with view access with each respective analyst country- group.
• C.Create a group per country. Add analysts to their respective country-groups. Create a single group
  'all_analysts', and add all country-groups as members. Grant the 'all-analysts' group the IAM role of BigQuery dataViewer. Share the appropriate dataset with view access with each respective analyst country- group.
• D.Create a group per country. Add analysts to their respective country-groups. Create a single group
  'all_analysts', and add all country-groups as members. Grant the 'all-analysts' group the IAM role of BigQuery dataViewer. Share the appropriate table with view access with each respective analyst country- group.

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Your company wants to track whether someone is present in a meeting room reserved for a scheduled meeting. There are 1000 meeting rooms across 5 offices on 3 continents. Each room is equipped with a motion sensor that reports its status every second. The data from the motion detector includes only a sensor ID and several different discrete items of information. Analysts will use this data, together with information about account owners and office locations. Which database type should you use?

• A.Flat file
• B.NoSQL
• C.Relational
• D.Blobstore

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Your application needs to process credit card transactions. You want the smallest scope of Payment Card Industry (PCI) compliance without compromising the ability to analyze transactional data and trends relating to which payment methods are used. How should you design your architecture?

• A.Create a tokenizer service and store only tokenized data.
• B.Create separate projects that only process credit card data.
• C.Create separate subnetworks and isolate the components that process credit card data.
• D.Streamline the audit discovery phase by labeling all of the virtual machines (VMs) that process PCI data.
• E.Enable Logging export to Google BigQuery and use ACLs and views to scope the data shared with the auditor.

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