You are helping the QA team to roll out a new load-testing tool to test the scalability of your primary cloud services that run on Google Compute Engine with Cloud Bigtable. Which three requirements should they include? Choose 3 answers

• A.Schedule the load-testing tool to regularly run against the production environment.
• B.Create a separate Google Cloud project to use for the load-testing environment.
• C.Ensure all third-party systems your services use are capable of handling high load.
• D.Instrument the load-testing tool and the target services with detailed logging and metrics collection.
• E.Ensure that the load tests validate the performance of Cloud Bigtable.
• F.Instrument the production services to record every transaction for replay by the load-testing tool.

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For this question, refer to the TerramEarth case study.
TerramEarth has equipped unconnected trucks with servers and sensors to collet telemetry data. Next year they want to use the data to train machine learning models. They want to store this data in the cloud while reducing costs. What should they do?

• A.Have the vehicle' computer compress the data in hourly snapshots, and store it in a Google Cloud storage (GCS) Nearline bucket.
• B.Push the telemetry data in Real-time to a streaming dataflow job that compresses the data, and store it in Google BigQuery.
• C.Push the telemetry data in real-time to a streaming dataflow job that compresses the data, and store it in Cloud Bigtable.
• D.Have the vehicle's computer compress the data in hourly snapshots, a Store it in a GCS Coldline bucket.

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You are monitoring Google Kubernetes Engine (GKE) clusters in a Cloud Monitoring workspace. As a Site Reliability Engineer (SRE), you need to triage incidents quickly. What should you do?

• A.Navigate the predefined dashboards in the Cloud Monitoring workspace, create custom metrics, and install alerting software on a Compute Engine instance.
• B.Navigate the predefined dashboards in the Cloud Monitoring workspace, and then add metrics and create alert policies.
• C.Write a shell script that gathers metrics from GKE nodes, publish these metrics to a Pub/Sub topic, export the data to BigQuery, and make a Data Studio dashboard.
• D.Create a custom dashboard in the Cloud Monitoring workspace for each incident, and then add metrics and create alert policies.

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Your customer runs a web service used by e-commerce sites to offer product recommendations to users. The company has begun experimenting with a machine learning model on Google Cloud Platform to improve the quality of results.
What should the customer do to improve their model's results over time?

• A.Export Cloud Machine Learning Engine performance metrics from Stackdriver to BigQuery, to be used to analyze the efficiency of the model.
• B.Monitor Compute Engine announcements for availability of newer CPU architectures, and deploy the model to them as soon as they are available for additional performance.
• C.Build a roadmap to move the machine learning model training from Cloud GPUs to Cloud TPUs, which offer better results.
• D.Save a history of recommendations and results of the recommendations in BigQuery, to be used as training data.

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You need to develop procedures to test a disaster plan for a mission-critical application. You want to use Google-recommended practices and native capabilities within GCP.
What should you do?

• A.Use Deployment Manager to automate service provisioning. Use Activity Logs to monitor and debug your tests.
• B.Use Deployment Manager to automate provisioning. Use Stackdriver to monitor and debug your tests.
• C.Use gcloud scripts to automate service provisioning. Use Activity Logs monitor and debug your tests.
• D.Use automated scripts to automate service provisioning. Use Activity Logs monitor and debug your tests.

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