Google Professional-Data-Engineer Exam With Confidence Using Practice Dumps

Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis

Load each month’s CSV data into BigQuery, and write a SQL query to transform the data to a standard schema. Merge the transformed tables together with a SQL query

Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformation. The Python code should be stored in a revision control system and modified as the incoming data’s schema changes

Use Apache Spark on Cloud Dataproc to infer the schema of the CSV file before creating a Dataframe. Then implement the transformations in Spark SQL before writing the data out to Cloud Storage and loading into BigQuery

Create a Cloud Dataproc Workflow Template

Create an initialization action to execute the jobs

Create a Directed Acyclic Graph in Cloud Composer

Create a Bash script that uses the Cloud SDK to create a cluster, execute jobs, and then tear down the cluster

Create a view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName.

Add a new column called FullName to the Users table. Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values.

Create a Google Cloud Dataflow job that queries BigQuery for the entire Users table, concatenates the FirstName value and LastName value for each user, and loads the proper values for FirstName, LastName, and FullName into a new table in BigQuery.

Use BigQuery to export the data for the table to a CSV file. Create a Google Cloud Dataproc job to process the CSV file and output a new CSV file containing the proper values for FirstName, LastName and FullName. Run a BigQuery load job to load the new CSV file into BigQuery.