Business owners at your company have given you a database of bank transactions. Each row contains the user ID, transaction type, transaction location, and transaction amount. They ask you to investigate what type of machine learning can be applied to the data. Which three machine learning applications can you use? (Choose three.)

• A.Supervised learning to determine which transactions are most likely to be fraudulent.
• B.Unsupervised learning to determine which transactions are most likely to be fraudulent.
• C.Clustering to divide the transactions into N categories based on feature similarity.
• D.Supervised learning to predict the location of a transaction.
• E.Reinforcement learning to predict the location of a transaction.
• F.Unsupervised learning to predict the location of a transaction.

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Your team is working on a binary classification problem. You have trained a support vector machine (SVM) classifier with default parameters, and received an area under the Curve (AUC) of 0.87 on the validation set. You want to increase the AUC of the model. What should you do?

• A.Perform hyperparameter tuning
• B.Train a classifier with deep neural networks, because neural networks would always beat SVMs
• C.Deploy the model and measure the real-world AUC; it's always higher because of generalization
• D.Scale predictions you get out of the model (tune a scaling factor as a hyperparameter) in order to get the highest AUC

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You are designing a cloud-native historical data processing system to meet the following conditions:
* The data being analyzed is in CSV, Avro, and PDF formats and will be accessed by multiple analysis tools including Cloud Dataproc, BigQuery, and Compute Engine.
* A streaming data pipeline stores new data daily.
* Peformance is not a factor in the solution.
* The solution design should maximize availability.
How should you design data storage for this solution?

• A.Store the data in a regional Cloud Storage bucket. Access the bucket directly using Cloud Dataproc, BigQuery, and Compute Engine.
• B.Create a Cloud Dataproc cluster with high availability. Store the data in HDFS, and peform analysis as needed.
• C.Store the data in a multi-regional Cloud Storage bucket. Access the data directly using Cloud Dataproc, BigQuery, and Compute Engine.
• D.Store the data in BigQuery. Access the data using the BigQuery Connector on Cloud Dataproc and Compute Engine.

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Your company is migrating their 30-node Apache Hadoop cluster to the cloud. They want to re-use Hadoop jobs they have already created and minimize the management of the cluster as much as possible. They also want to be able to persist data beyond the life of the cluster. What should you do?

• A.Create a Hadoop cluster on Google Compute Engine that uses Local SSD disks.
• B.Create a Google Cloud Dataflow job to process the data.
• C.Create a Hadoop cluster on Google Compute Engine that uses persistent disks.
• D.Create a Cloud Dataproc cluster that uses the Google Cloud Storage connector.
• E.Create a Google Cloud Dataproc cluster that uses persistent disks for HDFS.

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You have some data, which is shown in the graphic below. The two dimensions are X and Y, and the shade of each dot represents what class it is. You want to classify this data accurately using a linear algorithm. To do this you need to add a synthetic feature. What should the value of that feature be?

• A.X^2
• B.X^2+Y^2
• C.cos(X)
• D.Y^2

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