Online Access Free Professional-Data-Engineer Practice Test

Your weather app queries a database every 15 minutes to get the current temperature. The frontend is powered by Google App Engine and server millions of users. How should you design the frontend to respond to a database failure?

• A.Retry the query every second until it comes back online to minimize staleness of data.
• B.Issue a command to restart the database servers.
• C.Reduce the query frequency to once every hour until the database comes back online.
• D.Retry the query with exponential backoff, up to a cap of 15 minutes.

You have thousands of Apache Spark jobs running in your on-premises Apache Hadoop cluster. You want to migrate the jobs to Google Cloud. You want to use managed services to run your jobs instead of maintaining a long-lived Hadoop cluster yourself. You have a tight timeline and want to keep code changes to a minimum.
What should you do?

• A.Move your data to Cloud Storage. Run your jobs on Dataproc.
• B.Move your data to BigQuery. Convert your Spark scripts to a SQL-based processing approach.
• C.Copy your data to Compute Engine disks. Manage and run your jobs directly on those instances.
• D.Rewrite your jobs in Apache Beam. Run your jobs in Dataflow.

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

• A.If you write a query in Legacy SQL, it might generate an error if you try to run it with Standard SQL.
• B.You need to set a query language for each dataset and the default is Standard SQL.
• C.Standard SQL is the preferred query language for BigQuery.
• D.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).

You work for a bank. You have a labelled dataset that contains information on already granted loan application and whether these applications have been defaulted. You have been asked to train a model to predict default rates for credit applicants.
What should you do?

• A.Match loan applicants with their social profiles to enable feature engineering.
• B.Remove the bias from the data and collect applications that have been declined loans.
• C.Increase the size of the dataset by collecting additional data.
• D.Train a linear regression to predict a credit default risk score.

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