You have a job that you want to cancel. It is a streaming pipeline, and you want to ensure that any data that is in-flight is processed and written to the output. Which of the following commands can you use on the Dataflow monitoring console to stop the pipeline job?

• A.Cancel
• B.Drain
• C.Stop
• D.Finish

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Which of these is NOT a way to customize the software on Dataproc cluster instances?

• A.Set initialization actions
• B.Modify configuration files using cluster properties
• C.Configure the cluster using Cloud Deployment Manager
• D.Log into the master node and make changes from there

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Your company is migrating their 30-node Apache Hadoop cluster to the cloud. They want to re-use Hadoop jobs they have already created and minimize the management of the cluster as much as possible. They also want to be able to persist data beyond the life of the cluster. What should you do?

• A.Create a Google Cloud Dataflow job to process the data.
• B.Create a Google Cloud Dataproc cluster that uses persistent disks for HDFS.
• C.Create a Hadoop cluster on Google Compute Engine that uses persistent disks.
• D.Create a Cloud Dataproc cluster that uses the Google Cloud Storage connector.
• E.Create a Hadoop cluster on Google Compute Engine that uses Local SSD disks.

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You need to move 2 PB of historical data from an on-premises storage appliance to Cloud Storage within six months, and your outbound network capacity is constrained to 20 Mb/sec. How should you migrate this data to Cloud Storage?

• A.Use trickle or ionice along with gsutil cp to limit the amount of bandwidth gsutil utilizes to less than 20 Mb/sec so it does not interfere with the production traffic
• B.Use gsutil cp -J to compress the content being uploaded to Cloud Storage
• C.Use Transfer Appliance to copy the data to Cloud Storage
• D.Create a private URL for the historical data, and then use Storage Transfer Service to copy the data to Cloud Storage

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You are operating a streaming Cloud Dataflow pipeline. Your engineers have a new version of the pipeline with a different windowing algorithm and triggering strategy. You want to update the running pipeline with the new version. You want to ensure that no data is lost during the update. What should you do?

• A.Update the Cloud Dataflow pipeline inflight by passing the --update option with the --jobName set to the existing job name
• B.Update the Cloud Dataflow pipeline inflight by passing the --updateoption with the --jobNameset to a new unique job name
• C.Stop the Cloud Dataflow pipeline with the Cancel option. Create a new Cloud Dataflow job with the updated code
• D.Stop the Cloud Dataflow pipeline with the Drain option. Create a new Cloud Dataflow job with the updated code

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