Which of the following is NOT one of the three main types of triggers that Dataflow supports?

• A.Trigger based on element size in bytes
• B.Trigger that is a combination of other triggers
• C.Trigger based on element count
• D.Trigger based on time

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Which software libraries are supported by Cloud Machine Learning Engine?

• A.Theano and TensorFlow
• B.Theano and Torch
• C.TensorFlow
• D.TensorFlow and Torch

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Which of these statements about exporting data from BigQuery is false?

• A.To export more than 1 GB of data, you need to put a wildcard in the destination filename.
• B.The only supported export destination is Google Cloud Storage.
• C.Data can only be exported in JSON or Avro format.
• D.The only compression option available is GZIP.

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You are running a pipeline in Cloud Dataflow that receives messages from a Cloud Pub/Sub topic and writes the results to a BigQuery dataset in the EU. Currently, your pipeline is located in europe-west4 and has a maximum of 3 workers, instance type n1-standard-1. You notice that during peak periods, your pipeline is struggling to process records in a timely fashion, when all 3 workers are at maximum CPU utilization. Which two actions can you take to increase performance of your pipeline? (Choose two.)

• A.Change the zone of your Cloud Dataflow pipeline to run in us-central1
• B.Increase the number of max workers
• C.Create a temporary table in Cloud Spanner that will act as a buffer for new data. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Spanner to BigQuery
• D.Use a larger instance type for your Cloud Dataflow workers
• E.Create a temporary table in Cloud Bigtable that will act as a buffer for new data. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Bigtable to BigQuery

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You decided to use Cloud Datastore to ingest vehicle telemetry data in real time. You want to build a storage system that will account for the long-term data growth, while keeping the costs low. You also want to create snapshots of the data periodically, so that you can make a point-in-time (PIT) recovery, or clone a copy of the data for Cloud Datastore in a different environment. You want to archive these snapshots for a long time. Which two methods can accomplish this? (Choose two.)

• A.Write an application that uses Cloud Datastore client libraries to read all the entities. Treat each entity as a BigQuery table row via BigQuery streaming insert. Assign an export timestamp for each export, and attach it as an extra column for each row. Make sure that the BigQuery table is partitioned using the export timestamp column.
• B.Write an application that uses Cloud Datastore client libraries to read all the entities. Format the exported data into a JSON file. Apply compression before storing the data in Cloud Source Repositories.
• C.Use managed export, and store the data in a Cloud Storage bucket using Nearline or Coldline class.
• D.Use managed export, and then import the data into a BigQuery table created just for that export, and delete temporary export files.
• E.Use managed export, and then import to Cloud Datastore in a separate project under a unique namespace reserved for that export.

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