Your company maintains a hybrid deployment with GCP, where analytics are performed on your anonymized customer dat
a. The data are imported to Cloud Storage from your data center through parallel uploads to a data transfer server running on GCP. Management informs you that the daily transfers take too long and have
asked you to fix the problem. You want to maximize transfer speeds. Which action should you take?

• A.Increase the size of the Google Persistent Disk on your server.
• B.Increase your network bandwidth from Compute Engine to Cloud Storage.
• C.Increase the CPU size on your server.
• D.Increase your network bandwidth from your datacenter to GCP.

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Your organization has been collecting and analyzing data in Google BigQuery for 6 months. The majority
of the data analyzed is placed in a time-partitioned table named events_partitioned. To reduce the
cost of queries, your organization created a view called events, which queries only the last 14 days of
data. The view is described in legacy SQL. Next month, existing applications will be connecting to
BigQuery to read the eventsdata via an ODBC connection. You need to ensure the applications can
connect. Which two actions should you take? (Choose two.)

• A.Create a new view over events_partitioned using standard SQL
• B.Create a new partitioned table using a standard SQL query
• C.Create a service account for the ODBC connection to use for authentication
• D.Create a new view over events using standard SQL
• E.Create a Google Cloud Identity and Access Management (Cloud IAM) role for the ODBC connection
  and shared "events"

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You work for an advertising company, and you've developed a Spark ML model to predict click-through rates at advertisement blocks. You've been developing everything at your on-premises data center, and now your company is migrating to Google Cloud. Your data center will be migrated to BigQuery. You periodically retrain your Spark ML models, so you need to migrate existing training pipelines to Google Cloud. What should you do?

• A.Use Cloud ML Engine for training existing Spark ML models
• B.Rewrite your models on TensorFlow, and start using Cloud ML Engine
• C.Use Cloud Dataproc for training existing Spark ML models, but start reading data directly from BigQuery
• D.Spin up a Spark cluster on Compute Engine, and train Spark ML models on the data exported from BigQuery

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You are integrating one of your internal IT applications and Google BigQuery, so users can query BigQuery from the application's interface. You do not want individual users to authenticate to BigQuery and you do not want to give them access to the dataset. You need to securely access BigQuery from your IT application. What should you do?

• A.Create a dummy user and grant dataset access to that user. Store the username and password for that user in a file on the files system, and use those credentials to access the BigQuery dataset
• B.Create groups for your users and give those groups access to the dataset
• C.Create a service account and grant dataset access to that account. Use the service account's private key to access the dataset
• D.Integrate with a single sign-on (SSO) platform, and pass each user's credentials along with the query request

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A data scientist has created a BigQuery ML model and asks you to create an ML pipeline to serve predictions.
You have a REST API application with the requirement to serve predictions for an individual user ID with latency under 100 milliseconds. You use the following query to generate predictions: SELECT predicted_label, user_id FROM ML.PREDICT (MODEL 'dataset.model', table user_features). How should you create the ML pipeline?

• A.Create an Authorized View with the provided query. Share the dataset that contains the view with the application service account.
• B.Create a Cloud Dataflow pipeline using BigQueryIOto read predictions for all users from the query. Write the results to Cloud Bigtable using BigtableIO. Grant the Bigtable Reader role to the application service account so that the application can read predictions for individual users from Cloud Bigtable.
• C.Add a WHERE clause to the query, and grant the BigQuery Data Viewer role to the application service account.
• D.Create a Cloud Dataflow pipeline using BigQueryIOto read results from the query. Grant the Dataflow Worker role to the application service account.

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