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Question1: MJTelco Case StudyCompany OverviewMJTelco 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 BackgroundFounded 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 ConceptMJTelco 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 morethan 50,000 installations.Refine their machine-learning cycles to verify and improve the dynamic models they use to controltopology 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 RequirementsScale up their production environment with minimal cost, instantiating resources when and whereneeded 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 workersMaintain isolated environments that support rapid iteration of their machine-learning models withoutaffecting their customers.Technical RequirementsEnsure secure and efficient transport and storage of telemetry dataRapidly scale instances to support between 10,000 and 100,000 data providers with multiple flowseach.Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately100m records/daySupport rapid iteration of monitoring infrastructure focused on awareness of data pipeline problemsboth in telemetry flows and in production learning cycles.CEO StatementOur 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 StatementOur 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 StatementThe 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?
Question2: MJTelco Case StudyCompany OverviewMJTelco 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 BackgroundFounded 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 ConceptMJTelco 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 morethan 50,000 installations.Refine their machine-learning cycles to verify and improve the dynamic models they use to controltopology 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 RequirementsScale up their production environment with minimal cost, instantiating resources when and whereneeded 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 workersMaintain isolated environments that support rapid iteration of their machine-learning models withoutaffecting their customers.Technical RequirementsEnsure secure and efficient transport and storage of telemetry dataRapidly 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 approximately100m records/daySupport rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems both in telemetry flows and in production learning cycles.CEO StatementOur 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 StatementOur 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 StatementThe 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 visualization for operations teams with the following requirements:Telemetry must include data from all 50,000 installations for the most recent 6 weeks (sampling onceevery 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.You create a data source to store the last 6 weeks of data, and create visualizations that allow viewers to see multiple date ranges, distinct geographic regions, and unique installation types. You always show the latest data without any changes to your visualizations. You want to avoid creating and updating new visualizations each month. What should you do?
Question3: Your weather app queries a database every 15 minutes to get the current temperature. The frontend is powered by Google App Engine and server millions of users. How should you design the frontend to respond to a database failure?
Question4: Your financial services company is moving to cloud technology and wants to store 50 TB of financial time- series data in the cloud. This data is updated frequently and new data will be streaming in all the time.Your company also wants to move their existing Apache Hadoop jobs to the cloud to get insights into this data. Which product should they use to store the data?
Question5: 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?
Question6: Your company is loading comma-separated values (CSV) files into Google BigQuery. The data is fully imported successfully; however, the imported data is not matching byte-to-byte to the source file. What is the most likely cause of this problem?
Question7: You architect a system to analyze seismic data. Your extract, transform, and load (ETL) process runs as a series of MapReduce jobs on an Apache Hadoop cluster. The ETL process takes days to process a data set because some steps are computationally expensive. Then you discover that a sensor calibration step has been omitted. How should you change your ETL process to carry out sensor calibration systematically in the future?
Question8: Flowlogistic Case StudyCompany OverviewFlowlogistic 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 BackgroundThe 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 ConceptFlowlogistic wants to implement two concepts using the cloud:Use their proprietary technology in a real-time inventory-tracking system that indicates the location oftheir loadsPerform analytics on all their orders and shipment logs, which contain both structured and unstructureddata, 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 EnvironmentFlowlogistic 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 messages10 Kafka servers - tracking message aggregation and batch insertApplication servers - customer front end, middleware for order/customs- 60 virtual machines across 20 physical servers- Tomcat - Java services- Nginx - static content- Batch serversStorage appliances- iSCSI for virtual machine (VM) hosts- Fibre Channel storage area network (FC SAN) - SQL server storageNetwork-attached storage (NAS) image storage, logs, backups10 Apache Hadoop /Spark servers- Core Data Lake- Data analysis workloads20 miscellaneous servers- Jenkins, monitoring, bastion hosts,Business RequirementsBuild a reliable and reproducible environment with scaled panty of production.Aggregate data in a centralized Data Lake for analysisUse historical data to perform predictive analytics on future shipmentsAccurately track every shipment worldwide using proprietary technologyImprove business agility and speed of innovation through rapid provisioning of new resourcesAnalyze and optimize architecture for performance in the cloudMigrate fully to the cloud if all other requirements are metTechnical RequirementsHandle both streaming and batch dataMigrate existing Hadoop workloadsEnsure architecture is scalable and elastic to meet the changing demands of the company.Use managed services whenever possibleEncrypt data flight and at restConnect a VPN between the production data center and cloud environmentSEO StatementWe 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 StatementIT 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 StatementPart 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?
Question9: You launched a new gaming app almost three years ago. You have been uploading log files from the previous day to a separate Google BigQuery table with the table name format LOGS_yyyymmdd. You have been using table wildcard functions to generate daily and monthly reports for all time ranges.Recently, you discovered that some queries that cover long date ranges are exceeding the limit of 1,000 tables and failing. How can you resolve this issue?
Question10: You are responsible for writing your company's ETL pipelines to run on an Apache Hadoop cluster. The pipeline will require some checkpointing and splitting pipelines. Which method should you use to write the pipelines?
Question11: Your company receives both batch- and stream-based event data. You want to process the data using Google Cloud Dataflow over a predictable time period. However, you realize that in some instances data can arrive late or out of order. How should you design your Cloud Dataflow pipeline to handle data that is late or out of order?
Question12: Your company's on-premises Apache Hadoop servers are approaching end-of-life, and IT has decided to migrate the cluster to Google Cloud Dataproc. A like-for-like migration of the cluster would require 50 TB of Google Persistent Disk per node. The CIO is concerned about the cost of using that much block storage.You want to minimize the storage cost of the migration. What should you do?
Question13: Your globally distributed auction application allows users to bid on items. Occasionally, users place identical bids at nearly identical times, and different application servers process those bids. Each bid event contains the item, amount, user, and timestamp. You want to collate those bid events into a single location in real time to determine which user bid first. What should you do?
Question14: You want to use a database of information about tissue samples to classify future tissue samples as either normal or mutated. You are evaluating an unsupervised anomaly detection method for classifying the tissue samples. Which two characteristic support this method? (Choose two.)
Question15: You have spent a few days loading data from comma-separated values (CSV) files into the Google BigQuery table CLICK_STREAM. The column DTstores the epoch time of click events. For convenience, you chose a simple schema where every field is treated as the STRINGtype. 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?
Question16: 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?
Question17: 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?
Question18: You are using Google BigQuery as your data warehouse. Your users report that the following simple query is running very slowly, no matter when they run the query:SELECT country, state, city FROM [myproject:mydataset.mytable] GROUP BY country You check the query plan for the query and see the following output in the Read section of Stage:1:What is the most likely cause of the delay for this query?
Question19: Your neural network model is taking days to train. You want to increase the training speed. What can you do?
Question20: 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?
Question21: Your analytics team wants to build a simple statistical model to determine which customers are most likely to work with your company again, based on a few different metrics. They want to run the model on Apache Spark, using data housed in Google Cloud Storage, and you have recommended using Google Cloud Dataproc to execute this job. Testing has shown that this workload can run in approximately 30 minutes on a 15-node cluster, outputting the results into Google BigQuery. The plan is to run this workload weekly.How should you optimize the cluster for cost?
Question22: You have enabled the free integration between Firebase Analytics and Google BigQuery. Firebase now automatically creates a new table daily in BigQuery in the format app_events_YYYYMMDD.You want to query all of the tables for the past 30 days in legacy SQL. What should you do?
Question23: An organization maintains a Google BigQuery dataset that contains tables with user-level data. They want to expose aggregates of this data to other Google Cloud projects, while still controlling access to the user- level data. Additionally, they need to minimize their overall storage cost and ensure the analysis cost for other projects is assigned to those projects. What should they do?
Question24: You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform. Your user base could grow exponentially, but you do not want to manage infrastructure scaling.Which Google database service should you use?
Question25: You are building a data pipeline on Google Cloud. You need to prepare data using a casual method for a machine-learning process. You want to support a logistic regression model. You also need to monitor and adjust for null values, which must remain real-valued and cannot be removed. What should you do?
Question26: You are designing the database schema for a machine learning-based food ordering service that will predict what users want to eat. Here is some of the information you need to store:The user profile: What the user likes and doesn't like to eatThe user account information: Name, address, preferred meal timesThe order information: When orders are made, from where, to whomThe database will be used to store all the transactional data of the product. You want to optimize the data schema. Which Google Cloud Platform product should you use?
Question27: You are selecting services to write and transform JSON messages from Cloud Pub/Sub to BigQuery for a data pipeline on Google Cloud. You want to minimize service costs. You also want to monitor and accommodate input data volume that will vary in size with minimal manual intervention. What should you do?
Question28: 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?
Question29: You are developing an application that uses a recommendation engine on Google Cloud. Your solution should display new videos to customers based on past views. Your solution needs to generate labels for the entities in videos that the customer has viewed. Your design must be able to provide very fast filtering suggestions based on data from other customer preferences on several TB of data. What should you do?
Question30: 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?
Question31: Flowlogistic Case StudyCompany OverviewFlowlogistic 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 BackgroundThe 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 ConceptFlowlogistic wants to implement two concepts using the cloud:Use their proprietary technology in a real-time inventory-tracking system that indicates the location oftheir loadsPerform analytics on all their orders and shipment logs, which contain both structured and unstructureddata, 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 EnvironmentFlowlogistic architecture resides in a single data center:Databases8 physical servers in 2 clusters- SQL Server - user data, inventory, static data3 physical servers- Cassandra - metadata, tracking messages10 Kafka servers - tracking message aggregation and batch insertApplication servers - customer front end, middleware for order/customs60 virtual machines across 20 physical servers- Tomcat - Java services- Nginx - static content- Batch serversStorage appliances- iSCSI for virtual machine (VM) hosts- Fibre Channel storage area network (FC SAN) - SQL server storage- Network-attached storage (NAS) image storage, logs, backups10 Apache Hadoop /Spark servers- Core Data Lake- Data analysis workloads20 miscellaneous servers- Jenkins, monitoring, bastion hosts,Business RequirementsBuild a reliable and reproducible environment with scaled panty of production.Aggregate data in a centralized Data Lake for analysisUse historical data to perform predictive analytics on future shipmentsAccurately track every shipment worldwide using proprietary technologyImprove business agility and speed of innovation through rapid provisioning of new resourcesAnalyze and optimize architecture for performance in the cloudMigrate fully to the cloud if all other requirements are metTechnical RequirementsHandle both streaming and batch dataMigrate existing Hadoop workloadsEnsure architecture is scalable and elastic to meet the changing demands of the company.Use managed services whenever possibleEncrypt data flight and at restConnect a VPN between the production data center and cloud environmentSEO StatementWe 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 StatementIT 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 StatementPart 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?
Question32: 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.BigQueryIO.Read.named("ReadLogData").from("clouddataflow-readonly:samples.log_data")You want to improve the performance of this data read. What should you do?
Question33: Your organization has been collecting and analyzing data in Google BigQuery for 6 months. The majority of the data analyzed is placed in a time-partitioned table named events_partitioned. To reduce the cost of queries, your organization created a view called events, which queries only the last 14 days of data. The view is described in legacy SQL. Next month, existing applications will be connecting to BigQuery to read the eventsdata via an ODBC connection. You need to ensure the applications can connect. Which two actions should you take? (Choose two.)
Question34: MJTelco Case StudyCompany OverviewMJTelco 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 BackgroundFounded 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 ConceptMJTelco 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 morethan 50,000 installations.Refine their machine-learning cycles to verify and improve the dynamic models they use to controltopology 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 RequirementsScale up their production environment with minimal cost, instantiating resources when and whereneeded 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 workersMaintain isolated environments that support rapid iteration of their machine-learning models withoutaffecting their customers.Technical RequirementsEnsure secure and efficient transport and storage of telemetry dataRapidly scale instances to support between 10,000 and 100,000 data providers with multiple flowseach.Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately100m records/daySupport rapid iteration of monitoring infrastructure focused on awareness of data pipeline problemsboth in telemetry flows and in production learning cycles.CEO StatementOur 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 StatementOur 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 StatementThe 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?
Question35: You work for a manufacturing plant that batches application log files together into a single log file once a day at 2:00 AM. You have written a Google Cloud Dataflow job to process that log file. You need to make sure the log file in processed once per day as inexpensively as possible. What should you do?
Question36: You are integrating one of your internal IT applications and Google BigQuery, so users can query BigQuery from the application's interface. You do not want individual users to authenticate to BigQuery and you do not want to give them access to the dataset. You need to securely access BigQuery from your IT application. What should you do?
Question37: MJTelco Case StudyCompany OverviewMJTelco 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 BackgroundFounded 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 ConceptMJTelco 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 morethan 50,000 installations.Refine their machine-learning cycles to verify and improve the dynamic models they use to controltopology 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 RequirementsScale up their production environment with minimal cost, instantiating resources when and whereneeded 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 workersMaintain isolated environments that support rapid iteration of their machine-learning models withoutaffecting their customers.Technical RequirementsEnsure secure and efficient transport and storage of telemetry dataRapidly 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 approximately100m records/daySupport rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems both in telemetry flows and in production learning cycles.CEO StatementOur 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 StatementOur 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 StatementThe 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 is building a custom interface to share data. They have these requirements:1. They need to do aggregations over their petabyte-scale datasets.2. They need to scan specific time range rows with a very fast response time (milliseconds).Which combination of Google Cloud Platform products should you recommend?
Question38: Flowlogistic Case StudyCompany OverviewFlowlogistic 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 BackgroundThe 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 ConceptFlowlogistic wants to implement two concepts using the cloud:Use their proprietary technology in a real-time inventory-tracking system that indicates the location oftheir loadsPerform analytics on all their orders and shipment logs, which contain both structured and unstructureddata, 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 EnvironmentFlowlogistic architecture resides in a single data center:Databases8 physical servers in 2 clusters- SQL Server - user data, inventory, static data3 physical servers- Cassandra - metadata, tracking messages10 Kafka servers - tracking message aggregation and batch insertApplication servers - customer front end, middleware for order/customs60 virtual machines across 20 physical servers- Tomcat - Java services- Nginx - static content- Batch serversStorage appliances- iSCSI for virtual machine (VM) hosts- Fibre Channel storage area network (FC SAN) - SQL server storage- Network-attached storage (NAS) image storage, logs, backups10 Apache Hadoop /Spark servers- Core Data Lake- Data analysis workloads20 miscellaneous servers- Jenkins, monitoring, bastion hosts,Business RequirementsBuild a reliable and reproducible environment with scaled panty of production.Aggregate data in a centralized Data Lake for analysisUse historical data to perform predictive analytics on future shipmentsAccurately track every shipment worldwide using proprietary technologyImprove business agility and speed of innovation through rapid provisioning of new resourcesAnalyze and optimize architecture for performance in the cloudMigrate fully to the cloud if all other requirements are metTechnical RequirementsHandle both streaming and batch dataMigrate existing Hadoop workloadsEnsure architecture is scalable and elastic to meet the changing demands of the company.Use managed services whenever possibleEncrypt data flight and at restConnect a VPN between the production data center and cloud environmentSEO StatementWe 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 StatementIT 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 StatementPart 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?
Question39: Your company has recently grown rapidly and now ingesting data at a significantly higher rate than it was previously. You manage the daily batch MapReduce analytics jobs in Apache Hadoop. However, the recent increase in data has meant the batch jobs are falling behind. You were asked to recommend ways the development team could increase the responsiveness of the analytics without increasing costs. What should you recommend they do?
Question40: 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?
Question41: You set up a streaming data insert into a Redis cluster via a Kafka cluster. Both clusters are running on Compute Engine instances. You need to encrypt data at rest with encryption keys that you can create, rotate, and destroy as needed. What should you do?
