You need to store and analyze social media postings in Google BigQuery at a rate of 10,000 messages per minute in near real-time. Initially, design the application to use streaming inserts for individual postings. Your application also performs data aggregations right after the streaming inserts. You discover that the queries after streaming inserts do not exhibit strong consistency, and reports from the queries might miss in-flight data.
How can you adjust your application design?

• A.Re-write the application to load accumulated data every 2 minutes.
• B.Estimate the average latency for data availability after streaming inserts, and always run queries after waiting twice as long.
• C.Convert the streaming insert code to batch load for individual messages.
• D.Load the original message to Google Cloud SQL, and export the table every hour to BigQuery via streaming inserts.

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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.Preprocess all data using Dataflow. At prediction time, use BigQuery's ML.EVALUATE clause without specifying any further transformations on the 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.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.
• 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 are developing an application on Google Cloud that will automatically generate subject labels for users' blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning. What should you do?

• A.Call the Cloud Natural Language API from your application. Process the generated Entity Analysis as labels.
• B.Call the Cloud Natural Language API from your application. Process the generated Sentiment Analysis as labels.
• C.Build and train a text classification model using TensorFlow. Deploy the model using Cloud Machine Learning Engine. Call the model from your application and process the results as labels.
• D.Build and train a text classification model using TensorFlow. Deploy the model using a Kubernetes Engine cluster. Call the model from your application and process the results as labels.

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You have spent a few days loading data from comma-separated values (CSV) files into the Google
BigQuery table CLICK_STREAM. The column DTstores the epoch time of click events. For convenience,
you chose a simple schema where every field is treated as the STRINGtype. Now, you want to compute
web session durations of users who visit your site, and you want to change its data type to the
TIMESTAMP. You want to minimize the migration effort without making future queries computationally
expensive. What should you do?

• A.Construct a query to return every row of the table CLICK_STREAM, while using the built-in function to
  cast strings from the column DTinto TIMESTAMPvalues. Run the query into a destination table
  NEW_CLICK_STREAM, in which the column TSis the TIMESTAMPtype. Reference the table
  NEW_CLICK_STREAMinstead of the table CLICK_STREAMfrom now on. In the future, new data is
  loaded into the table NEW_CLICK_STREAM.
• B.Add a column TSof the TIMESTAMPtype to the table CLICK_STREAM, and populate the numeric
  values from the column TSfor each row. Reference the column TSinstead of the column DTfrom now
  on.
• C.Create a view CLICK_STREAM_V, where strings from the column DTare cast into TIMESTAMPvalues.
  Reference the view CLICK_STREAM_Vinstead of the table CLICK_STREAMfrom now on.
• D.Delete the table CLICK_STREAM, and then re-create it such that the column DTis of the TIMESTAMP
  type. Reload the data.
• E.Add two columns to the table CLICK STREAM: TSof the TIMESTAMPtype and IS_NEWof the
  BOOLEANtype. Reload all data in append mode. For each appended row, set the value of IS_NEWto
  true. For future queries, reference the column TSinstead of the column DT, with the WHEREclause
  ensuring that the value of IS_NEWmust be true.

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Your company uses a proprietary system to send inventory data every 6 hours to a data ingestion service
in the cloud. Transmitted data includes a payload of several fields and the timestamp of the transmission. If
there are any concerns about a transmission, the system re-transmits the data. How should you
deduplicate the data most efficiency?

• A.Maintain a database table to store the hash value and other metadata for each data entry.
• B.Compute the hash value of each data entry, and compare it with all historical data.
• C.Assign global unique identifiers (GUID) to each data entry.
• D.Store each data entry as the primary key in a separate database and apply an index.

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