You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally. You also want to optimize data for range queries on nonkey columns. What should you do?

• A. Use Cloud SQL for storag
• B. Use Cloud Spanner for storag
• C. Add secondary indexes to support query patterns.
• D. Use Cloud Dataflow to transform data to support query patterns.
• E. Use Cloud Spanner for storag
• F. Use Cloud SQL for storag
• G. Use Cloud Dataflow to transform data to support query patterns.
• H. Add secondary indexes to support query patterns.

As your organization expands its usage of GCP, many teams have started to create their own projects. Projects are further multiplied to accommodate different stages of deployments and target audiences. Each project requires unique access control configurations. The central IT team needs to have access to all projects. Furthermore, data from Cloud Storage buckets and BigQuery datasets must be shared for use in other projects in an ad hoc way. You want to simplify access control management by minimizing the number of policies. Which two steps should you take? Choose 2 answers.

• A. Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.
• B. For each Cloud Storage bucket or BigQuery dataset, decide which projects need acces
• C. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
• D. Introduce resource hierarchy to leverage access control policy inheritance.
• E. Use Cloud Deployment Manager to automate access provision.
• F. Create distinct groups for various teams, and specify groups in Cloud IAM policies.

Suppose you have a table that includes a nested column called "city" inside a column called "person", but when you try to submit the following query in BigQuery, it gives you an error.
SELECT person FROM `project1.example.table1` WHERE city = "London"
How would you correct the error?

• A. Add ", UNNEST(city)" before the WHERE clause.
• B. Change "person" to "person.city".
• C. Change "person" to "city.person".
• D. Add ", UNNEST(person)" before the WHERE clause.

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 FirstName field concatenated with a space, followed by the value of the LastName 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.
• B. 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 FullNam G. Run a BigQuery load job to load the new CSV file into BigQuery.
• C. Use BigQuery to export the data for the table to a CSV fil
• D. 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.
• E. Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values.
• F. Add a new column called FullName to the Users tabl

MJTelco is building a custom interface to share dat
a. They have these requirements:
They need to do aggregations over their petabyte-scale datasets.
They need to scan specific time range rows with a very fast response time (milliseconds). Which combination of Google Cloud Platform products should you recommend?

• A. Cloud Datastore and Cloud Bigtable
• B. BigQuery and Cloud Bigtable
• C. Cloud Bigtable and Cloud SQL
• D. BigQuery and Cloud Storage