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 non-key columns. What should you do?

• A.Use Cloud SQL for storage. Add secondary indexes to support query patterns.
• B.Use Cloud SQL for storage. Use Cloud Dataflow to transform data to support query patterns.
• C.Use Cloud Spanner for storage. Add secondary indexes to support query patterns.
• D.Use Cloud Spanner for storage. Use Cloud Dataflow to transform data to support query patterns.

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You need to create a near real-time inventory dashboard that reads the main inventory tables in your BigQuery data warehouse. Historical inventory data is stored as inventory balances by item and location. You have several thousand updates to inventory every hour. You want to maximize performance of the dashboard and ensure that the data is accurate. What should you do?

• A.Use the BigQuery streaming the stream changes into a daily inventory movement table. Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.
• B.Partition the inventory balance table by item to reduce the amount of data scanned with each inventory update.
• C.Use the BigQuery bulk loader to batch load inventory changes into a daily inventory movement table.
  Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.
• D.Leverage BigQuery UPDATE statements to update the inventory balances as they are changing.

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You are migrating a table to BigQuery and are deeding on the data model. Your table stores information related to purchases made across several store locations and includes information like the time of the transaction, items purchased, the store ID and the city and state in which the store is located You frequently query this table to see how many of each item were sold over the past 30 days and to look at purchasing trends by state city and individual store. You want to model this table to minimize query time and cost. What should you do?

• A.Top-level cluster by store ID first, then city then state.
• B.Partition by transaction time; cluster by state first, then city then store ID
• C.Top-level cluster by stale first, then city then store
• D.Partition by transaction tome cluster by store ID first, then city, then stale

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What are two of the characteristics of using online prediction rather than batch prediction?

• A.It is optimized to handle a high volume of data instances in a job and to run more complex models.
• B.Predictions are returned in the response message.
• C.Predictions are written to output files in a Cloud Storage location that you specify.
• D.It is optimized to minimize the latency of serving predictions.

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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.For each Cloud Storage bucket or BigQuery dataset, decide which projects need access. 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.Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
• C.Introduce resource hierarchy to leverage access control policy inheritance.
• D.Use Cloud Deployment Manager to automate access provision.
• E.Create distinct groups for various teams, and specify groups in Cloud IAM policies.

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