PDE — questions

Your company uses a proprietary system to send inventory data every 6 hours to a data ingestion service in the cloud. Transmitted data includes a payload of several fields and the timestamp of the transmission. If there are any concerns about a transmission, the system re-transmits the data. How should you deduplicate the data most efficiency?

Your company has hired a new data scientist who wants to perform complicated analyses across very large datasets stored in Google Cloud Storage and in a Cassandra cluster on Google Compute Engine. The scientist primarily wants to create labelled data sets for machine learning projects, along with some visualization tasks. She reports that her laptop is not powerful enough to perform her tasks and it is slowing her down. You want to help her perform her tasks. What should you do?

You are deploying 10,000 new Internet of Things devices to collect temperature data in your warehouses globally. You need to process, store and analyze these very large datasets in real time. What should you do?

You have spent a few days loading data from comma-separated values (CSV) files into the Google BigQuery table CLICK_STREAM. The column DT stores the epoch time of click events. For convenience, you chose a simple schema where every field is treated as the STRING type. Now, you want to compute web session durations of users who visit your site, and you want to change its data type to the TIMESTAMP. You want to minimize the migration effort without making future queries computationally expensive. What should you do?

You want to use Google Stackdriver Logging to monitor Google BigQuery usage. You need an instant notification to be sent to your monitoring tool when new data is appended to a certain table using an insert job, but you do not want to receive notifications for other tables. What should you do?

You are working on a sensitive project involving private user data. You have set up a project on Google Cloud Platform to house your work internally. An external consultant is going to assist with coding a complex transformation in a Google Cloud Dataflow pipeline for your project. How should you maintain users' privacy?

You are building a model to predict whether or not it will rain on a given day. You have thousands of input features and want to see if you can improve training speed by removing some features while having a minimum effect on model accuracy. What can you do?

Your company is performing data preprocessing for a learning algorithm in Google Cloud Dataflow. Numerous data logs are being are being generated during this step, and the team wants to analyze them. Due to the dynamic nature of the campaign, the data is growing exponentially every hour. The data scientists have written the following code to read the data for a new key features in the logs. You want to improve the performance of this data read. What should you do?

Your company is streaming real-time sensor data from their factory floor into Bigtable and they have noticed extremely poor performance. How should the row key be redesigned to improve Bigtable performance on queries that populate real-time dashboards?

Your company's customer and order databases are often under heavy load. This makes performing analytics against them difficult without harming operations. The databases are in a MySQL cluster, with nightly backups taken using mysqldump. You want to perform analytics with minimal impact on operations. What should you do?

You have Google Cloud Dataflow streaming pipeline running with a Google Cloud Pub/Sub subscription as the source. You need to make an update to the code that will make the new Cloud Dataflow pipeline incompatible with the current version. You do not want to lose any data when making this update. What should you do?

Your company is running their first dynamic campaign, serving different offers by analyzing real-time data during the holiday season. The data scientists are collecting terabytes of data that rapidly grows every hour during their 30-day campaign. They are using Google Cloud Dataflow to preprocess the data and collect the feature (signals) data that is needed for the machine learning model in Google Cloud Bigtable. The team is observing suboptimal performance with reads and writes of their initial load of 10 TB of data. They want to improve this performance while minimizing cost. What should they do?

Your software uses a simple JSON format for all messages. These messages are published to Google Cloud Pub/Sub, then processed with Google Cloud Dataflow to create a real-time dashboard for the CFO. During testing, you notice that some messages are missing in the dashboard. You check the logs, and all messages are being published to Cloud Pub/Sub successfully. What should you do next?

Flowlogistic Case Study - Company Overview - Flowlogistic is a leading logistics and supply chain provider. They help businesses throughout the world manage their resources and transport them to their final destination. The company has grown rapidly, expanding their offerings to include rail, truck, aircraft, and oceanic shipping. Company Background - The company started as a regional trucking company, and then expanded into other logistics market. Because they have not updated their infrastructure, managing and tracking orders and shipments has become a bottleneck. To improve operations, Flowlogistic developed proprietary technology for tracking shipments in real time at the parcel level. However, they are unable to deploy it because their technology stack, based on Apache Kafka, cannot support the processing volume. In addition, Flowlogistic wants to further analyze their orders and shipments to determine how best to deploy their resources. Solution Concept - Flowlogistic wants to implement two concepts using the cloud: • Use their proprietary technology in a real-time inventory-tracking system that indicates the location of their loads • Perform analytics on all their orders and shipment logs, which contain both structured and unstructured data, to determine how best to deploy resources, which markets to expand info. They also want to use predictive analytics to learn earlier when a shipment will be delayed. Existing Technical Environment - Flowlogistic architecture resides in a single data center: • Databases 8 physical servers in 2 clusters - SQL Server `" user data, inventory, static data 3 physical servers - Cassandra `" metadata, tracking messages 10 Kafka servers `" tracking message aggregation and batch insert • Application servers `" customer front end, middleware for order/customs 60 virtual machines across 20 physical servers - Tomcat `" Java services - Nginx `" static content - Batch servers • Storage appliances - iSCSI for virtual machine (VM) hosts - Fibre Channel storage area network (FC SAN) `" SQL server storage - Network-attached storage (NAS) image storage, logs, backups • 10 Apache Hadoop /Spark servers - Core Data Lake - Data analysis workloads • 20 miscellaneous servers - Jenkins, monitoring, bastion hosts, Business Requirements - Build a reliable and reproducible environment with scaled panty of production. • Aggregate data in a centralized Data Lake for analysis • Use historical data to perform predictive analytics on future shipments • Accurately track every shipment worldwide using proprietary technology • Improve business agility and speed of innovation through rapid provisioning of new resources • Analyze and optimize architecture for performance in the cloud • Migrate fully to the cloud if all other requirements are met Technical Requirements - • Handle both streaming and batch data • Migrate existing Hadoop workloads • Ensure architecture is scalable and elastic to meet the changing demands of the company. • Use managed services whenever possible • Encrypt data flight and at rest • Connect a VPN between the production data center and cloud environment SEO Statement - We have grown so quickly that our inability to upgrade our infrastructure is really hampering further growth and efficiency. We are efficient at moving shipments around the world, but we are inefficient at moving data around. We need to organize our information so we can more easily understand where our customers are and what they are shipping. CTO Statement - IT has never been a priority for us, so as our data has grown, we have not invested enough in our technology. I have a good staff to manage IT, but they are so busy managing our infrastructure that I cannot get them to do the things that really matter, such as organizing our data, building the analytics, and figuring out how to implement the CFO' s tracking technology. CFO Statement - Part of our competitive advantage is that we penalize ourselves for late shipments and deliveries. Knowing where out shipments are at all times has a direct correlation to our bottom line and profitability. Additionally, I don't want to commit capital to building out a server environment. Flowlogistic wants to use Google BigQuery as their primary analysis system, but they still have Apache Hadoop and Spark workloads that they cannot move to BigQuery. Flowlogistic does not know how to store the data that is common to both workloads. What should they do?

Flowlogistic Case Study - Company Overview - Flowlogistic is a leading logistics and supply chain provider. They help businesses throughout the world manage their resources and transport them to their final destination. The company has grown rapidly, expanding their offerings to include rail, truck, aircraft, and oceanic shipping. Company Background - The company started as a regional trucking company, and then expanded into other logistics market. Because they have not updated their infrastructure, managing and tracking orders and shipments has become a bottleneck. To improve operations, Flowlogistic developed proprietary technology for tracking shipments in real time at the parcel level. However, they are unable to deploy it because their technology stack, based on Apache Kafka, cannot support the processing volume. In addition, Flowlogistic wants to further analyze their orders and shipments to determine how best to deploy their resources. Solution Concept - Flowlogistic wants to implement two concepts using the cloud: • Use their proprietary technology in a real-time inventory-tracking system that indicates the location of their loads • Perform analytics on all their orders and shipment logs, which contain both structured and unstructured data, to determine how best to deploy resources, which markets to expand info. They also want to use predictive analytics to learn earlier when a shipment will be delayed. Existing Technical Environment - Flowlogistic architecture resides in a single data center: • Databases 8 physical servers in 2 clusters - SQL Server `" user data, inventory, static data 3 physical servers - Cassandra `" metadata, tracking messages 10 Kafka servers `" tracking message aggregation and batch insert • Application servers `" customer front end, middleware for order/customs 60 virtual machines across 20 physical servers - Tomcat `" Java services - Nginx `" static content - Batch servers • Storage appliances - iSCSI for virtual machine (VM) hosts - Fibre Channel storage area network (FC SAN) `" SQL server storage - Network-attached storage (NAS) image storage, logs, backups • 10 Apache Hadoop /Spark servers - Core Data Lake - Data analysis workloads • 20 miscellaneous servers - Jenkins, monitoring, bastion hosts, Business Requirements - • Build a reliable and reproducible environment with scaled panty of production. • Aggregate data in a centralized Data Lake for analysis • Use historical data to perform predictive analytics on future shipments • Accurately track every shipment worldwide using proprietary technology • Improve business agility and speed of innovation through rapid provisioning of new resources • Analyze and optimize architecture for performance in the cloud • Migrate fully to the cloud if all other requirements are met Technical Requirements - • Handle both streaming and batch data • Migrate existing Hadoop workloads • Ensure architecture is scalable and elastic to meet the changing demands of the company. • Use managed services whenever possible • Encrypt data flight and at rest • Connect a VPN between the production data center and cloud environment SEO Statement - We have grown so quickly that our inability to upgrade our infrastructure is really hampering further growth and efficiency. We are efficient at moving shipments around the world, but we are inefficient at moving data around. We need to organize our information so we can more easily understand where our customers are and what they are shipping. CTO Statement - IT has never been a priority for us, so as our data has grown, we have not invested enough in our technology. I have a good staff to manage IT, but they are so busy managing our infrastructure that I cannot get them to do the things that really matter, such as organizing our data, building the analytics, and figuring out how to implement the CFO' s tracking technology. CFO Statement - Part of our competitive advantage is that we penalize ourselves for late shipments and deliveries. Knowing where out shipments are at all times has a direct correlation to our bottom line and profitability. Additionally, I don't want to commit capital to building out a server environment. Flowlogistic's management has determined that the current Apache Kafka servers cannot handle the data volume for their real-time inventory tracking system. You need to build a new system on Google Cloud Platform (GCP) that will feed the proprietary tracking software. The system must be able to ingest data from a variety of global sources, process and query in real-time, and store the data reliably. Which combination of GCP products should you choose?

Flowlogistic Case Study - Company Overview - Flowlogistic is a leading logistics and supply chain provider. They help businesses throughout the world manage their resources and transport them to their final destination. The company has grown rapidly, expanding their offerings to include rail, truck, aircraft, and oceanic shipping. Company Background - The company started as a regional trucking company, and then expanded into other logistics market. Because they have not updated their infrastructure, managing and tracking orders and shipments has become a bottleneck. To improve operations, Flowlogistic developed proprietary technology for tracking shipments in real time at the parcel level. However, they are unable to deploy it because their technology stack, based on Apache Kafka, cannot support the processing volume. In addition, Flowlogistic wants to further analyze their orders and shipments to determine how best to deploy their resources. Solution Concept - Flowlogistic wants to implement two concepts using the cloud: Use their proprietary technology in a real-time inventory-tracking system that indicates the location of their loads • Perform analytics on all their orders and shipment logs, which contain both structured and unstructured data, to determine how best to deploy resources, which markets to expand info. They also want to use predictive analytics to learn earlier when a shipment will be delayed. Existing Technical Environment - Flowlogistic architecture resides in a single data center: • Databases 8 physical servers in 2 clusters - SQL Server `" user data, inventory, static data 3 physical servers - Cassandra `" metadata, tracking messages 10 Kafka servers `" tracking message aggregation and batch insert • Application servers `" customer front end, middleware for order/customs 60 virtual machines across 20 physical servers - Tomcat `" Java services - Nginx `" static content - Batch servers • Storage appliances - iSCSI for virtual machine (VM) hosts - Fibre Channel storage area network (FC SAN) `" SQL server storage - Network-attached storage (NAS) image storage, logs, backups • 10 Apache Hadoop /Spark servers - Core Data Lake - Data analysis workloads • 20 miscellaneous servers - Jenkins, monitoring, bastion hosts, Business Requirements - • Build a reliable and reproducible environment with scaled panty of production. • Aggregate data in a centralized Data Lake for analysis • Use historical data to perform predictive analytics on future shipments • Accurately track every shipment worldwide using proprietary technology • Improve business agility and speed of innovation through rapid provisioning of new resources • Analyze and optimize architecture for performance in the cloud • Migrate fully to the cloud if all other requirements are met Technical Requirements - Handle both streaming and batch data • Migrate existing Hadoop workloads • Ensure architecture is scalable and elastic to meet the changing demands of the company. • Use managed services whenever possible • Encrypt data flight and at rest • Connect a VPN between the production data center and cloud environment SEO Statement - We have grown so quickly that our inability to upgrade our infrastructure is really hampering further growth and efficiency. We are efficient at moving shipments around the world, but we are inefficient at moving data around. We need to organize our information so we can more easily understand where our customers are and what they are shipping. CTO Statement - IT has never been a priority for us, so as our data has grown, we have not invested enough in our technology. I have a good staff to manage IT, but they are so busy managing our infrastructure that I cannot get them to do the things that really matter, such as organizing our data, building the analytics, and figuring out how to implement the CFO' s tracking technology. CFO Statement - Part of our competitive advantage is that we penalize ourselves for late shipments and deliveries. Knowing where out shipments are at all times has a direct correlation to our bottom line and profitability. Additionally, I don't want to commit capital to building out a server environment. 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?

Flowlogistic Case Study - Company Overview - Flowlogistic is a leading logistics and supply chain provider. They help businesses throughout the world manage their resources and transport them to their final destination. The company has grown rapidly, expanding their offerings to include rail, truck, aircraft, and oceanic shipping. Company Background - The company started as a regional trucking company, and then expanded into other logistics market. Because they have not updated their infrastructure, managing and tracking orders and shipments has become a bottleneck. To improve operations, Flowlogistic developed proprietary technology for tracking shipments in real time at the parcel level. However, they are unable to deploy it because their technology stack, based on Apache Kafka, cannot support the processing volume. In addition, Flowlogistic wants to further analyze their orders and shipments to determine how best to deploy their resources. Solution Concept - Flowlogistic wants to implement two concepts using the cloud: • Use their proprietary technology in a real-time inventory-tracking system that indicates the location of their loads • Perform analytics on all their orders and shipment logs, which contain both structured and unstructured data, to determine how best to deploy resources, which markets to expand info. They also want to use predictive analytics to learn earlier when a shipment will be delayed. Existing Technical Environment - Flowlogistic architecture resides in a single data center: • Databases 8 physical servers in 2 clusters - SQL Server `" user data, inventory, static data 3 physical servers - Cassandra `" metadata, tracking messages 10 Kafka servers `" tracking message aggregation and batch insert • Application servers `" customer front end, middleware for order/customs 60 virtual machines across 20 physical servers - Tomcat `" Java services - Nginx `" static content - Batch servers • Storage appliances - iSCSI for virtual machine (VM) hosts - Fibre Channel storage area network (FC SAN) `" SQL server storage - Network-attached storage (NAS) image storage, logs, backups • 10 Apache Hadoop /Spark servers - Core Data Lake - Data analysis workloads • 20 miscellaneous servers - Jenkins, monitoring, bastion hosts, Business Requirements - • Build a reliable and reproducible environment with scaled panty of production. • Aggregate data in a centralized Data Lake for analysis • Use historical data to perform predictive analytics on future shipments • Accurately track every shipment worldwide using proprietary technology • Improve business agility and speed of innovation through rapid provisioning of new resources • Analyze and optimize architecture for performance in the cloud • Migrate fully to the cloud if all other requirements are met Technical Requirements - • Handle both streaming and batch data • Migrate existing Hadoop workloads • Ensure architecture is scalable and elastic to meet the changing demands of the company. • Use managed services whenever possible • Encrypt data flight and at rest • Connect a VPN between the production data center and cloud environment SEO Statement - We have grown so quickly that our inability to upgrade our infrastructure is really hampering further growth and efficiency. We are efficient at moving shipments around the world, but we are inefficient at moving data around. We need to organize our information so we can more easily understand where our customers are and what they are shipping. CTO Statement - IT has never been a priority for us, so as our data has grown, we have not invested enough in our technology. I have a good staff to manage IT, but they are so busy managing our infrastructure that I cannot get them to do the things that really matter, such as organizing our data, building the analytics, and figuring out how to implement the CFO' s tracking technology. CFO Statement - Part of our competitive advantage is that we penalize ourselves for late shipments and deliveries. Knowing where out shipments are at all times has a direct correlation to our bottom line and profitability. Additionally, I don't want to commit capital to building out a server environment. Flowlogistic is rolling out their real-time inventory tracking system. The tracking devices will all send package-tracking messages, which will now go to a single Google Cloud Pub/Sub topic instead of the Apache Kafka cluster. A subscriber application will then process the messages for real-time reporting and store them in Google BigQuery for historical analysis. You want to ensure the package data can be analyzed over time. Which approach should you take?

MJTelco Case Study - Company Overview - MJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the world. The company has patents for innovative optical communications hardware. Based on these patents, they can create many reliable, high-speed backbone links with inexpensive hardware. Company Background - Founded by experienced telecom executives, MJTelco uses technologies originally developed to overcome communications challenges in space. Fundamental to their operation, they need to create a distributed data infrastructure that drives real-time analysis and incorporates machine learning to continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the network allowing them to account for the impact of dynamic regional politics on location availability and cost. Their management and operations teams are situated all around the globe creating many-to-many relationship between data consumers and provides in their system. After careful consideration, they decided public cloud is the perfect environment to support their needs. Solution Concept - MJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs: • Scale and harden their PoC to support significantly more data flows generated when they ramp to more than 50,000 installations. • Refine their machine-learning cycles to verify and improve the dynamic models they use to control topology definition. MJTelco will also use three separate operating environments `" development/test, staging, and production `" to meet the needs of running experiments, deploying new features, and serving production customers. Business Requirements - • Scale up their production environment with minimal cost, instantiating resources when and where needed in an unpredictable, distributed telecom user community. • Ensure security of their proprietary data to protect their leading-edge machine learning and analysis. • Provide reliable and timely access to data for analysis from distributed research workers • Maintain isolated environments that support rapid iteration of their machine-learning models without affecting their customers. Technical Requirements - • Ensure secure and efficient transport and storage of telemetry data • Rapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows each. • Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately 100m records/day • Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems both in telemetry flows and in production learning cycles. CEO Statement - Our business model relies on our patents, analytics and dynamic machine learning. Our inexpensive hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize our large distributed data pipelines to meet our reliability and capacity commitments. CTO Statement - Our public cloud services must operate as advertised. We need resources that scale and keep our data secure. We also need environments in which our data scientists can carefully study and quickly adapt our models. Because we rely on automation to process our data, we also need our development and test environments to work as we iterate. CFO Statement - The project is too large for us to maintain the hardware and software required for the data and analysis. Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on automation and infrastructure. Google Cloud's machine learning will allow our quantitative researchers to work on our high-value problems instead of problems with our data pipelines. MJTelco's Google Cloud Dataflow pipeline is now ready to start receiving data from the 50,000 installations. You want to allow Cloud Dataflow to scale its compute power up as required. Which Cloud Dataflow pipeline configuration setting should you update?

MJTelco Case Study - Company Overview - MJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the world. The company has patents for innovative optical communications hardware. Based on these patents, they can create many reliable, high-speed backbone links with inexpensive hardware. Company Background - Founded by experienced telecom executives, MJTelco uses technologies originally developed to overcome communications challenges in space. Fundamental to their operation, they need to create a distributed data infrastructure that drives real-time analysis and incorporates machine learning to continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the network allowing them to account for the impact of dynamic regional politics on location availability and cost. Their management and operations teams are situated all around the globe creating many-to-many relationship between data consumers and provides in their system. After careful consideration, they decided public cloud is the perfect environment to support their needs. Solution Concept - MJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs: • Scale and harden their PoC to support significantly more data flows generated when they ramp to more than 50,000 installations. • Refine their machine-learning cycles to verify and improve the dynamic models they use to control topology definition. MJTelco will also use three separate operating environments `" development/test, staging, and production `" to meet the needs of running experiments, deploying new features, and serving production customers. Business Requirements - • Scale up their production environment with minimal cost, instantiating resources when and where needed in an unpredictable, distributed telecom user community. • Ensure security of their proprietary data to protect their leading-edge machine learning and analysis. • Provide reliable and timely access to data for analysis from distributed research workers Maintain isolated environments that support rapid iteration of their machine-learning models without affecting their customers. Technical Requirements - • Ensure secure and efficient transport and storage of telemetry data • Rapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows each. • Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately 100m records/day • Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems both in telemetry flows and in production learning cycles. CEO Statement - Our business model relies on our patents, analytics and dynamic machine learning. Our inexpensive hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize our large distributed data pipelines to meet our reliability and capacity commitments. CTO Statement - Our public cloud services must operate as advertised. We need resources that scale and keep our data secure. We also need environments in which our data scientists can carefully study and quickly adapt our models. Because we rely on automation to process our data, we also need our development and test environments to work as we iterate. CFO Statement - The project is too large for us to maintain the hardware and software required for the data and analysis. Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on automation and infrastructure. Google Cloud's machine learning will allow our quantitative researchers to work on our high-value problems instead of problems with our data pipelines. You need to compose visualizations for operations teams with the following requirements: • The report must include telemetry data from all 50,000 installations for the most resent 6 weeks (sampling once every minute). • The report must not be more than 3 hours delayed from live data. • The actionable report should only show suboptimal links. • Most suboptimal links should be sorted to the top. • Suboptimal links can be grouped and filtered by regional geography. • User response time to load the report must be <5 seconds. Which approach meets the requirements?

MJTelco Case Study - Company Overview - MJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the world. The company has patents for innovative optical communications hardware. Based on these patents, they can create many reliable, high-speed backbone links with inexpensive hardware. Company Background - Founded by experienced telecom executives, MJTelco uses technologies originally developed to overcome communications challenges in space. Fundamental to their operation, they need to create a distributed data infrastructure that drives real-time analysis and incorporates machine learning to continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the network allowing them to account for the impact of dynamic regional politics on location availability and cost. Their management and operations teams are situated all around the globe creating many-to-many relationship between data consumers and provides in their system. After careful consideration, they decided public cloud is the perfect environment to support their needs. Solution Concept - MJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs: • Scale and harden their PoC to support significantly more data flows generated when they ramp to more than 50,000 installations. • Refine their machine-learning cycles to verify and improve the dynamic models they use to control topology definition. MJTelco will also use three separate operating environments `" development/test, staging, and production `" to meet the needs of running experiments, deploying new features, and serving production customers. Business Requirements - • Scale up their production environment with minimal cost, instantiating resources when and where needed in an unpredictable, distributed telecom user community. • Ensure security of their proprietary data to protect their leading-edge machine learning and analysis. Provide reliable and timely access to data for analysis from distributed research workers • Maintain isolated environments that support rapid iteration of their machine-learning models without affecting their customers. Technical Requirements - • Ensure secure and efficient transport and storage of telemetry data • Rapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows each. • Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately 100m records/day • Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems both in telemetry flows and in production learning cycles. CEO Statement - Our business model relies on our patents, analytics and dynamic machine learning. Our inexpensive hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize our large distributed data pipelines to meet our reliability and capacity commitments. CTO Statement - Our public cloud services must operate as advertised. We need resources that scale and keep our data secure. We also need environments in which our data scientists can carefully study and quickly adapt our models. Because we rely on automation to process our data, we also need our development and test environments to work as we iterate. CFO Statement - The project is too large for us to maintain the hardware and software required for the data and analysis. Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on automation and infrastructure. Google Cloud's machine learning will allow our quantitative researchers to work on our high-value problems instead of problems with our data pipelines. You create a new report for your large team in Google Data Studio 360. The report uses Google BigQuery as its data source. It is company policy to ensure employees can view only the data associated with their region, so you create and populate a table for each region. You need to enforce the regional access policy to the data. Which two actions should you take? (Choose two.)