Your startup has never implemented a formal security policy. Currently, everyone in the company has access to the datasets stored in Google BigQuery. Teams have freedom to use the service as they see fit, and they have not documented their use cases. You have been asked to secure the data warehouse. You need to discover what everyone is doing. What should you do first?

• A.Use the Google Cloud Billing API to see what account the warehouse is being billed to.
• B.Use Stackdriver Monitoring to see the usage of BigQuery query slots.
• C.Use Google Stackdriver Audit Logs to review data access.
• D.Get the identity and access management IIAM) policy of each table

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You have spent a few days loading data from comma-separated values (CSV) files into the Google BigQuery table CLICK_STREAM. The column DT stores the epoch time of click events. For convenience, you chose a simple schema where every field is treated as the STRING type. Now, you want to compute web session durations of users who visit your site, and you want to change its data type to the TIMESTAMP. You want to minimize the migration effort without making future queries computationally expensive. What should you do?

• A.Delete the table CLICK_STREAM, and then re-create it such that the column DT is of the TIMESTAMP type. Reload the data.
• B.Add a column TS of the TIMESTAMP type to the table CLICK_STREAM, and populate the numeric values from the column TS for each row. Reference the column TS instead of the column DT from now on.
• C.Create a view CLICK_STREAM_V, where strings from the column DT are cast into TIMESTAMP values. Reference the view CLICK_STREAM_V instead of the table CLICK_STREAM from now on.
• D.Construct a query to return every row of the table CLICK_STREAM, while using the built-in function to cast strings from the column DT into TIMESTAMP values. Run the query into a destination table NEW_CLICK_STREAM, in which the column TS is the TIMESTAMP type. Reference the table NEW_CLICK_STREAM instead of the table CLICK_STREAM from now on. In the future, new data is loaded into the table NEW_CLICK_STREAM.
• E.Add two columns to the table CLICK STREAM: TS of the TIMESTAMP type and IS_NEW of the BOOLEAN type. Reload all data in append mode. For each appended row, set the value of IS_NEW to true. For future queries, reference the column TS instead of the column DT, with the WHERE clause ensuring that the value of IS_NEW must be true.

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You are building an application to share financial market data with consumers, who will receive data feeds. Data is collected from the markets in real time. Consumers will receive the data in the following ways:
Real-time event stream
ANSI SQL access to real-time stream and historical data
Batch historical exports
Which solution should you use?

• A.Cloud Pub/Sub, Cloud Storage, BigQuery
• B.Cloud Dataproc, Cloud Dataflow, BigQuery
• C.Cloud Dataflow, Cloud SQL, Cloud Spanner
• D.Cloud Pub/Sub, Cloud Dataproc, Cloud SQL

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You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?

• A.Load the data every 30 minutes into a new partitioned table in BigQuery.
• B.Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery
• C.Store the data in Google Cloud Datastore. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore
• D.Store the data in a file in a regional Google Cloud Storage bucket. Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.

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You work for a mid-sized enterprise that needs to move its operational system transaction data from an on- premises database to GCP. The database is about 20 TB in size. Which database should you choose?

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

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