Which of these operations can you perform from the BigQuery Web UI?

• A.Upload a file in SQL format.
• B.Load data with nested and repeated fields.
• C.Upload a 20 MB file.
• D.Upload multiple files using a wildcard.

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You work for a large real estate firm and are preparing 6 TB of home sales data to be used for machine learning. You will use SQL to transform the data and use BigQuery ML to create a machine learning model. You plan to use the model for predictions against a raw dataset that has not been transformed. How should you set up your workflow in order to prevent skew at prediction time?

• A.Use a BigQuery view to define your preprocessing logic. When creating your model, use the view as your model training data. At prediction time, use BigQuery's ML.EVALUATE clause without specifying any transformations on the raw input data.
• B.When creating your model, use BigQuery's TRANSFORM clause to define preprocessing steps. Before requesting predictions, use a saved query to transform your raw input data, and then use ML.EVALUATE.
• C.Preprocess all data using Dataflow. At prediction time, use BigQuery's ML.EVALUATE clause without specifying any further transformations on the input data.
• D.When creating your model, use BigQuery's TRANSFORM clause to define preprocessing steps. At prediction time, use BigQuery's ML.EVALUATE clause without specifying any transformations on the raw input data.

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You operate a database that stores stock trades and an application that retrieves average stock price for a given company over an adjustable window of time. The data is stored in Cloud Bigtable where the datetime of the stock trade is the beginning of the row key. Your application has thousands of concurrent users, and you notice that performance is starting to degrade as more stocks are added. What should you do to improve the performance of your application?

• A.Use Cloud Dataflow to write summary of each day's stock trades to an Avro file on Cloud Storage. Update your application to read from Cloud Storage and Cloud Bigtable to compute the responses.
• B.Change the row key syntax in your Cloud Bigtable table to begin with a random number per second.
• C.Change the data pipeline to use BigQuery for storing stock trades, and update your application.
• D.Change the row key syntax in your Cloud Bigtable table to begin with the stock symbol.

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You need to compose visualization for operations teams with the following requirements:
* Telemetry must include data from all 50,000 installations for the most recent 6 weeks (sampling once every minute)
* The report must not be more than 3 hours delayed from live data.
* The actionable report should only show suboptimal links.
* Most suboptimal links should be sorted to the top.
* Suboptimal links can be grouped and filtered by regional geography.
* User response time to load the report must be <5 seconds.
You create a data source to store the last 6 weeks of data, and create visualizations that allow viewers to see multiple date ranges, distinct geographic regions, and unique installation types. You always show the latest data without any changes to your visualizations. You want to avoid creating and updating new visualizations each month. What should you do?

• A.Load the data into relational database tables, write a Google App Engine application that queries all rows, summarizes the data across each criteria, and then renders results using the Google Charts and visualization API.
• B.Look through the current data and compose a series of charts and tables, one for each possible combination of criteria.
• C.Look through the current data and compose a small set of generalized charts and tables bound to criteria filters that allow value selection.
• D.Export the data to a spreadsheet, compose a series of charts and tables, one for each possible combination of criteria, and spread them across multiple tabs.

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Which Java SDK class can you use to run your Dataflow programs locally?

• A.LocalRunner
• B.DirectPipelineRunner
• C.MachineRunner
• D.LocalPipelineRunner

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