You are tasked with building an online analytical processing (OLAP) marketing analytics and reporting tool.
This requires a relational database that can operate on hundreds of terabytes of data. What is the Google recommended tool for such applications?

• A.Cloud Spanner, because it is globally distributed
• B.Cloud SQL, because it is a fully managed relational database
• C.Cloud Firestore, because it offers real-time synchronization across devices
• D.BigQuery, because it is designed for large-scale processing of tabular data

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Your company plans to migrate a multi-petabyte data set to the cloud. The data set must be available 24hrs a day. Your business analysts have experience only with using a SQL interface. How should you store the data to optimize it for ease of analysis?

• A.Load data into Google BigQuery.
• B.Insert data into Google Cloud SQL.
• C.Put flat files into Google Cloud Storage.
• D.Stream data into Google Cloud Datastore.

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You want to automate the creation of a managed instance group. The VMs have many OS package dependencies. You want to minimize the startup time for VMs in the instance group.
What should you do?

• A.Use Terraform to create the managed instance group and a startup script to install the OS package dependencies.
• B.Create a custom VM image with all OS package dependencies. Use Deployment Manager to create the managed instance group with the VM image.
• C.Use Puppet to create the managed instance group and install the OS package dependencies.
• D.Use Deployment Manager to create the managed instance group and Ansible to install the OS package dependencies.

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You are working with a data warehousing team that performs data analysis. The team needs to process data from external partners, but the data contains personally identifiable information (PlI). You need to process and store the data without storing any of the Pll data. What should you do?

• A.Create a Dataflow pipeline to retrieve the data from the external sources. As part of the pipeline use the Cloud Data Loss Prevention (Cloud DLP) API to remove any Pll data Store the result in BigQuery
• B.Create a Dataflow pipeline to retrieve the data from the external sources. As part of the pipeline store all non-PII data in BigQuery and store all Pll data in a Cloud Storage bucket that has a retention policy set.
• C.Ask the external partners to upload an data on Cloud Storage Configure Bucket Lock for the bucket Create a Dataflow pipeline to read the data from the bucket As part of the pipeline, use the Cloud Data Loss Prevention (Cloud DIP) API to remove any Pll data Store the result in BigQuery
• D.Ask the external partners to import ail data in your BigQuery dataset Create a dataflow pipeline to copy the data into a new table As part of the Dataflow bucket skip all data in columns that have Pll data

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For this question, refer to the EHR Healthcare case study. You are responsible for ensuring that EHR's use of Google Cloud will pass an upcoming privacy compliance audit. What should you do? (Choose two.)

• A.Verify EHR's product usage against the list of compliant products on the Google Cloud compliance page.
• B.Advise EHR to execute a Business Associate Agreement (BAA) with Google Cloud.
• C.Use Firebase Authentication for EHR's user facing applications.
• D.Implement Prometheus to detect and prevent security breaches on EHR's web-based applications.
• E.Use GKE private clusters for all Kubernetes workloads.

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