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Your development team has created a mobile game app. You want to test the new mobile app on Android and

iOS devices with a variety of configurations. You need to ensure that testing is efficient and cost-effective. What

should you do?

A.

Upload your mobile app to the Firebase Test Lab, and test the mobile app on Android and iOS devices.

B.

Create Android and iOS VMs on Google Cloud, install the mobile app on the VMs, and test the mobile app.

C.

Create Android and iOS containers on Google Kubernetes Engine (GKE), install the mobile app on the

containers, and test the mobile app.

D.

Upload your mobile app with different configurations to Firebase Hosting and test each configuration.

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.

Deploy failure injection software to the game analytics platform that can inject additional latency to mobile client analytics traffic.

B.

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.

C.

Add the ability to introduce a random amount of delay before beginning to process analytics files uploaded from mobile devices.

D.

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.

You need to optimize batch file transfers into Cloud Storage for Mountkirk Games’ new Google Cloud solution.

The batch files contain game statistics that need to be staged in Cloud Storage and be processed by an extract

transform load (ETL) tool. What should you do?

A.

Use gsutil to batch move files in sequence.

B.

Use gsutil to batch copy the files in parallel.

C.

Use gsutil to extract the files as the first part of ETL.

D.

Use gsutil to load the files as the last part of ETL.

Mountkirk Games wants to limit the physical location of resources to their operating Google Cloud regions.

What should you do?

A.

Configure an organizational policy which constrains where resources can be deployed.

B.

Configure IAM conditions to limit what resources can be configured.

C.

Configure the quotas for resources in the regions not being used to 0.

D.

Configure a custom alert in Cloud Monitoring so you can disable resources as they are created in other

regions.

For this question, refer to the Mountkirk Games case study. Mountkirk Games wants to migrate from their current analytics and statistics reporting model to one that meets their technical requirements on Google Cloud Platform.

Which two steps should be part of their migration plan? (Choose two.)

A.

Evaluate the impact of migrating their current batch ETL code to Cloud Dataflow.

B.

Write a schema migration plan to denormalize data for better performance in BigQuery.

C.

Draw an architecture diagram that shows how to move from a single MySQL database to a MySQL cluster.

D.

Load 10 TB of analytics data from a previous game into a Cloud SQL instance, and run test queries against the full dataset to confirm that they complete successfully.

E.

Integrate Cloud Armor to defend against possible SQL injection attacks in analytics files uploaded to Cloud Storage.

For this question, refer to the Mountkirk Games case study. Mountkirk Games wants to design their solution for the future in order to take advantage of cloud and technology improvements as they become available. Which two steps should they take? (Choose two.)

A.

Store as much analytics and game activity data as financially feasible today so it can be used to train machine learning models to predict user behavior in the future.

B.

Begin packaging their game backend artifacts in container images and running them on Kubernetes Engine to improve the availability to scale up or down based on game activity.

C.

Set up a CI/CD pipeline using Jenkins and Spinnaker to automate canary deployments and improve development velocity.

D.

Adopt a schema versioning tool to reduce downtime when adding new game features that require storing additional player data in the database.

E.

Implement a weekly rolling maintenance process for the Linux virtual machines so they can apply critical kernel patches and package updates and reduce the risk of 0-day vulnerabilities.

Your development teams release new versions of games running on Google Kubernetes Engine (GKE) daily.

You want to create service level indicators (SLIs) to evaluate the quality of the new versions from the user’s

perspective. What should you do?

A.

Create CPU Utilization and Request Latency as service level indicators.

B.

Create GKE CPU Utilization and Memory Utilization as service level indicators.

C.

Create Request Latency and Error Rate as service level indicators.

D.

Create Server Uptime and Error Rate as service level indicators.

For this question refer to the TerramEarth case study

Operational parameters such as oil pressure are adjustable on each of TerramEarth's vehicles to increase their efficiency, depending on their environmental conditions. Your primary goal is to increase the operating efficiency of all 20 million cellular and unconnected vehicles in the field How can you accomplish this goal?

A.

Have your engineers inspect the data for patterns, and then create an algorithm with rules that make operational adjustments automatically.

B.

Capture all operating data, train machine learning models that identify ideal operations, and run locally to make operational adjustments automatically.

C.

Implement a Google Cloud Dataflow streaming job with a sliding window, and use Google Cloud Messaging (GCM) to make operational adjustments automatically.

D.

Capture all operating data, train machine learning models that identify ideal operations, and host in Google Cloud Machine Learning (ML) Platform to make operational adjustments automatically.

For this question, refer to the TerramEarth case study

Your development team has created a structured API to retrieve vehicle data. They want to allow third parties to develop tools for dealerships that use this vehicle event data. You want to support delegated authorization against this data. What should you do?

A.

Build or leverage an OAuth-compatible access control system.

B.

Build SAML 2.0 SSO compatibility into your authentication system.

C.

Restrict data access based on the source IP address of the partner systems.

D.

Create secondary credentials for each dealer that can be given to the trusted third party.

For this question, refer to the TerramEarth case study.

The TerramEarth development team wants to create an API to meet the company's business requirements. You want the development team to focus their development effort on business value versus creating a custom framework. Which method should they use?

A.

Use Google App Engine with Google Cloud Endpoints. Focus on an API for dealers and partners.

B.

Use Google App Engine with a JAX-RS Jersey Java-based framework. Focus on an API for the public.

C.

Use Google App Engine with the Swagger (open API Specification) framework. Focus on an API for the public.

D.

Use Google Container Engine with a Django Python container. Focus on an API for the public.

E.

Use Google Container Engine with a Tomcat container with the Swagger (Open API Specification) framework. Focus on an API for dealers and partners.