Suppose you have a dataset of images that are each labeled as to whether or not they contain a human face. To create a neural network that recognizes human faces in images using this labeled dataset, what approach would likely be the most effective?

• A.Use K-means Clustering to detect faces in the pixels.
• B.Use feature engineering to add features for eyes, noses, and mouths to the input data.
• C.Use deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.
• D.Build a neural network with an input layer of pixels, a hidden layer, and an output layer with two categories.

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You use BigQuery as your centralized analytics platform. New data is loaded every day, and an ETL pipeline modifies the original data and prepares it for the final users. This ETL pipeline is regularly modified and can generate errors, but sometimes the errors are detected only after 2 weeks. You need to provide a method to recover from these errors, and your backups should be optimized for storage costs. How should you organize your data in BigQuery and store your backups?

• A.Organize your data in separate tables for each month, and use snapshot decorators to restore the table to a time prior to the corruption.
• B.Organize your data in separate tables for each month, and duplicate your data on a separate dataset in BigQuery.
• C.Organize your data in separate tables for each month, and export, compress, and store the data in Cloud Storage.
• D.Organize your data in a single table, export, and compress and store the BigQuery data in Cloud Storage.

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You are developing an application that uses a recommendation engine on Google Cloud. Your solution
should display new videos to customers based on past views. Your solution needs to generate labels for
the entities in videos that the customer has viewed. Your design must be able to provide very fast filtering
suggestions based on data from other customer preferences on several TB of data. What should you do?

• A.Build and train a classification model with Spark MLlib to generate labels. Build and train a second
  classification model with Spark MLlib to filter results to match customer preferences. Deploy the
  models using Cloud Dataproc. Call the models from your application.
• B.Build and train a complex classification model with Spark MLlib to generate labels and filter the results.
  Deploy the models using Cloud Dataproc. Call the model from your application.
• C.Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud
  Bigtable, and filter the predicted labels to match the user's viewing history to generate preferences.
• D.Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud
  SQL, and join and filter the predicted labels to match the user's viewing history to generate
  preferences.

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Which of the following statements about Legacy SQL and Standard SQL is not true?

• A.Standard SQL is the preferred query language for BigQuery.
• B.If you write a query in Legacy SQL, it might generate an error if you try to run it with Standard SQL.
• C.One difference between the two query languages is how you specify fully-qualified table names (i.e. table names that include their associated project name).
• D.You need to set a query language for each dataset and the default is Standard SQL.

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Your neural network model is taking days to train. You want to increase the training speed. What can you do?

• A.Subsample your test dataset.
• B.Subsample your training dataset.
• C.Increase the number of input features to your model.
• D.Increase the number of layers in your neural network.

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