An online retailer has built their current application on Google App Engine. A new initiative at the company mandates that they extend their application to allow their customers to transact directly via the application.
They need to manage their shopping transactions and analyze combined data from multiple datasets using a business intelligence (BI) tool. They want to use only a single database for this purpose. Which Google Cloud database should they choose?

• A. BigQuery
• B. Cloud Datastore
• C. Cloud SQL
• D. Cloud BigTable

You are planning to migrate your current on-premises Apache Hadoop deployment to the cloud. You need to ensure that the deployment is as fault-tolerant and cost-effective as possible for long-running batch jobs. You want to use a managed service. What should you do?

• A. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
• B. Use an SSD persistent disk and 50% preemptible worker
• C. Change references in scripts from hdfs:// to gs://
• D. Use a standard persistent disk and 50% preemptible worker
• E. Install Hadoop and Spark on a 10-node Compute Engine instance group with standard instance
• F. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
• G. Install Hadoop and Spark on a 10-node Compute Engine instance group with preemptible instances.Store data in HDF
• H. Change references in scripts from hdfs:// to gs://
• I. Install the Cloud Storage connector, and store the data in Cloud Storag
• J. Deploy a Cloud Dataproc cluste
• K. Deploy a Cloud Dataproc cluste

Which is not a valid reason for poor Cloud Bigtable performance?

• A. The table's schema is not designed correctly.
• B. The Cloud Bigtable cluster has too many nodes.
• C. The workload isn't appropriate for Cloud Bigtable.
• D. There are issues with the network connection.

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Each analytics team in your organization is running BigQuery jobs in their own projects. You want to enable each team to monitor slot usage within their projects. What should you do?

• A. Create a Stackdriver Monitoring dashboard based on the BigQuery metric query/scanned_bytes
• B. Create a Stackdriver Monitoring dashboard based on the BigQuery metric slots/allocated_for_project
• C. Create an aggregated log export at the organization level, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric
• D. Create a log export for each project, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric

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