Your company has successfully migrated to the cloud and wants to analyze their data stream to optimize operations. They do not have any existing code for this analysis, so they are exploring all their options. These options include a mix of batch and stream processing, as they are running some hourly jobs and live-processing some data as it comes in. Which technology should they use for this?

• A.Google Cloud Dataproc
• B.Google Cloud Dataflow
• C.Google Container Engine with Bigtable
• D.Google Compute Engine with Google BigQuery

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For this question, refer to the JencoMart case study.
JencoMart has built a version of their application on Google Cloud Platform that serves traffic to Asi a. You want to measure success against their business and technical goals. Which metrics should you track?

• A.Error rates for requests from Asia
• B.Latency difference between US and Asia
• C.Total visits, error rates, and latency from Asia
• D.Total visits and average latency for users in Asia
• E.The number of character sets present in the database

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Mountkirk Games has deployed their new backend on Google Cloud Platform (GCP). You want to create a
through testing process for new versions of the backend before they are released to the public. You want
the testing environment to scale in an economical way. How should you design the process?

• A.Create a scalable environment in GCP for simulating production load
• B.Use the existing infrastructure to test the GCP-based backend at scale
• C.Build stress tests into each component of your application using resources internal to GCP to simulate
  load
• D.Create a set of static environments in GCP to test different levels of load - for example, high, medium,
  and low

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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.Move all the data into 1 region, then launch a Google Cloud Dataproc cluster to run the job.
• C.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.
• D.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 .....

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Your architecture calls for the centralized collection of all admin activity and VM system logs within your project.
How should you collect these logs from both VMs and services?

• A.All admin and VM system logs are automatically collected by Stackdriver.
• B.Stackdriver automatically collects admin activity logs for most services. The Stackdriver Logging agent must be installed on each instance to collect system logs.
• C.Launch a custom syslogd compute instance and configure your GCP project and VMs to forward all logs to it.
• D.Install the Stackdriver Logging agent on a single compute instance and let it collect all audit and access logs for your environment.

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