For this question, refer to the Mountkirk Games case study. Mountkirk Games wants you to design a way
to test the analytics platform's resilience to changes in mobile network latency. What should you do?

• A.Build a test client that can be run from a mobile phone emulator on a Compute Engine virtual machine,
  and run multiple copies in Google Cloud Platform regions all over the world to generate realistic traffic.
• B.Create an opt-in beta of the game that runs on players' mobile devices and collects response times
  from analytics endpoints running in Google Cloud Platform regions all over the world.
• C.Add the ability to introduce a random amount of delay before beginning to process analytics files
  uploaded from mobile devices.
• D.Deploy failure injection software to the game analytics platform that can inject additional latency to
  mobile client analytics traffic.

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For this question, refer to the TerramEarth case study.
To speed up data retrieval, more vehicles will be upgraded to cellular connections and be able to transmit data to the ETL process. The current FTP process is error-prone and restarts the data transfer from the start of the file when connections fail, which happens often. You want to improve the reliability of the solution and minimize data transfer time on the cellular connections. What should you do?

• A.Directly transfer the files to different Google Cloud Multi-Regional Storage bucket locations in us, eu, and asia using Google APIs over HTTP(S). Run the ETL process using the data in the bucket.
• B.Use one Google Container Engine cluster of FTP servers. Save the data to a Multi- Regional bucket. Run the ETL process using data in the bucket.
• C.Use multiple Google Container Engine clusters running FTP servers located in different regions. Save the data to Multi-Regional buckets in us, eu, and asia. Run the ETL process using the data in the bucket.
• D.Directly transfer the files to a different Google Cloud Regional Storage bucket location in us, eu, and asia using Google APIs over HTTP(S). Run the ETL process to retrieve the data from each Regional bucket.

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For this question, refer to the TerramEarth case study.
TerramEarth's 20 million vehicles are scattered around the world. Based on the vehicle's location its telemetry data is stored in a Google Cloud Storage (GCS) regional bucket (US. Europe, or Asia). The CTO has asked you to run a report on the raw telemetry data to determine why vehicles are breaking down after 100 K miles. You want to run this job on all the data. What is the most cost-effective way to run this job?

• A.Move all the data into 1 zone, then launch a Cloud Dataproc cluster to run the job.
• B.Launch a cluster in each region to preprocess and compress the raw data, then move the data into a multi region bucket and use a Dataproc cluster to finish the job.
• C.Launch a cluster in each region to preprocess and compress the raw data, then move the data into a regional bucket and use a Cloud Dataproc cluster .....
• D.Move all the data into 1 region, then launch a Google Cloud Dataproc cluster to run the job.

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Your company is migrating its on-premises data center into the cloud. As part of the migration, you want to integrate Kubernetes Engine for workload orchestration. Parts of your architecture must also be PCI DSScompliant.
Which of the following is most accurate?

• A.App Engine is the only compute platform on GCP that is certified for PCI DSS hosting.
• B.Kubernetes Engine cannot be used under PCI DSS because it is considered shared hosting.
• C.Kubernetes Engine and GCP provide the tools you need to build a PCI DSS-compliant environment.
• D.All Google Cloud services are usable because Google Cloud Platform is certified PCI-compliant.

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Your application needs to process credit card transactions. You want the smallest scope of Payment Card Industry (PCI) compliance without compromising the ability to analyze transactional data and trends relating to which payment methods are used. How should you design your architecture?

• A.Enable Logging export to Google BigQuery and use ACLs and views to scope the data shared with the auditor.
• B.Create separate projects that only process credit card data.
• C.Create a tokenizer service and store only tokenized data.
• D.Create separate subnetworks and isolate the components that process credit card data.
• E.Streamline the audit discovery phase by labeling all of the virtual machines (VMs) that process PCI data.

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