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Google Data Engineer Sample Questions:

1. You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform. Your user base could grow exponentially, but you do not want to manage infrastructure scaling.
Which Google database service should you use?

A) Cloud Datastore
B) Cloud Bigtable
C) BigQuery
D) Cloud SQL

2. What are two methods that can be used to denormalize tables in BigQuery?

A) 1) Use a partitioned table; 2) Join tables into one table
B) 1) Split table into multiple tables; 2) Use a partitioned table
C) 1) Join tables into one table; 2) Use nested repeated fields
D) 1) Use nested repeated fields; 2) Use a partitioned table

3. You work for a global shipping company. You want to train a model on 40 TB of data to predict which ships in each geographic region are likely to cause delivery delays on any given day. The model will be based on multiple attributes collected from multiple sources. Telemetry data, including location in GeoJSON format, will be pulled from each ship and loaded every hour. You want to have a dashboard that shows how many and which ships are likely to cause delays within a region. You want to use a storage solution that has native functionality for prediction and geospatial processing. Which storage solution should you use?

A) Cloud Datastore
B) Cloud SQL for PostgreSQL
C) Cloud Bigtable
D) BigQuery

4. Which is not a valid reason for poor Cloud Bigtable performance?

A) The Cloud Bigtable cluster has too many nodes.
B) There are issues with the network connection.
C) The table's schema is not designed correctly.
D) The workload isn't appropriate for Cloud Bigtable.

5. You receive data files in CSV format monthly from a third party. You need to cleanse this data, but every third month the schema of the files changes. Your requirements for implementing these transformations include:
Executing the transformations on a schedule
Enabling non-developer analysts to modify transformations
Providing a graphical tool for designing transformations
What should you do?

A) Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard scheme
B) Merge the transformed tables together with a SQL query
C) Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis
D) The Python code should be stored in a revision control system and modified as the incoming data's schema changes
E) 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
F) Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformatio

Solutions: