Flowlogistic's CEO wants to gain rapid insight into their customer base so his sales team can be better informed in the field. This team is not very technical, so they've purchased a visualization tool to simplify the creation of BigQuery reports. However, they've been overwhelmed by all the data in the table, and are spending a lot of money on queries trying to find the data they need. You want to solve their problem in the most cost-effective way. What should you do?

• A. Create an additional table with only the necessary columns.
• B. Create a view on the table to present to the virtualization tool.
• C. Export the data into a Google Sheet for virtualization.
• D. Create identity and access management (IAM) roles on the appropriate columns, so only they appear in a query.

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

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

You are building a new data pipeline to share data between two different types of applications: jobs generators and job runners. Your solution must scale to accommodate increases in usage and must accommodate the addition of new applications without negatively affecting the performance of existing ones. What should you do?

• A. Create an API using App Engine to receive and send messages to the applications
• B. Use a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them
• C. Create a table on Cloud Spanner, and insert and delete rows with the job information
• D. Create a table on Cloud SQL, and insert and delete rows with the job information