You work for a large fast food restaurant chain with over 400,000 employees. You store employee information in Google BigQuery in a Users table consisting of a FirstName field and a LastName field. A member of IT is building an application and asks you to modify the schema and data in BigQuery so the application can query a FullName field consisting of the value of the field concatenated with a space, followed by the value of the field for each employee. How can you make that data available while minimizing cost?
A) Create a view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName . Create a view in BigQuery that concatenates the and field values to produce the .
B) Add a new column called FullName to the Users table. Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values. Add a new column called to the Users table. Run an UPDATE statement that updates the column for each user with the concatenation of the and values.
C) Create a Google Cloud Dataflow job that queries BigQuery for the entire Users table, concatenates the FirstName value and LastName value for each user, and loads the proper values for FirstName , LastName , and FullName into a new table in BigQuery. Create a Google Cloud Dataflow job that queries BigQuery for the entire table, concatenates the value and value for each user, and loads the proper values for , , and into a new table in BigQuery.
D) Use BigQuery to export the data for the table to a CSV file. Create a Google Cloud Dataproc job to process the CSV file and output a new CSV file containing the proper values for FirstName , LastName and FullName . Run a BigQuery load job to load the new CSV file into BigQuery. Use BigQuery to export the data for the table to a CSV file. Create a Google Cloud Dataproc job to process the CSV file and output a new CSV file containing the proper values for , . Run a BigQuery load job to load the new CSV file into BigQuery.

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The Answer of You work for a large fast food restaurant chain with over 400,000 employees. You store employee information in Google BigQuery in a Users table consisting of a FirstName field and a LastName field. A member of IT is building an application and asks you to modify the schema and data in BigQuery so the application can query a FullName field consisting of the value of the field concatenated with a space, followed by the value of the field for each employee. How can you make that data available while minimizing cost?
A) Create a view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName . Create a view in BigQuery that concatenates the and field values to produce the .
B) Add a new column called FullName to the Users table. Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values. Add a new column called to the Users table. Run an UPDATE statement that updates the column for each user with the concatenation of the and values.
C) Create a Google Cloud Dataflow job that queries BigQuery for the entire Users table, concatenates the FirstName value and LastName value for each user, and loads the proper values for FirstName , LastName , and FullName into a new table in BigQuery. Create a Google Cloud Dataflow job that queries BigQuery for the entire table, concatenates the value and value for each user, and loads the proper values for , , and into a new table in BigQuery.
D) Use BigQuery to export the data for the table to a CSV file. Create a Google Cloud Dataproc job to process the CSV file and output a new CSV file containing the proper values for FirstName , LastName and FullName . Run a BigQuery load job to load the new CSV file into BigQuery. Use BigQuery to export the data for the table to a CSV file. Create a Google Cloud Dataproc job to process the CSV file and output a new CSV file containing the proper values for , . Run a BigQuery load job to load the new CSV file into BigQuery.

You Work for a Large Fast Food Restaurant Chain with Over