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Google Data Engineer Sample Questions:

1. You are a retailer that wants to integrate your online sales capabilities with different in-home assistants, such as Google Home. You need to interpret customer voice commands and issue an order to the backend systems. Which solutions should you choose?

A) Cloud Speech-to-Text API
B) Dialogflow Enterprise Edition
C) Cloud Natural Language API
D) Cloud AutoML Natural Language

2. Which Cloud Dataflow / Beam feature should you use to aggregate data in an unbounded data source every hour based on the time when the data entered the pipeline?

A) An hourly watermark
B) The with Allowed Lateness method
C) A processing time trigger
D) An event time trigger

3. Your company maintains a hybrid deployment with GCP, where analytics are performed on your anonymized customer dat a. The data are imported to Cloud Storage from your data center through parallel uploads to a data transfer server running on GCP. Management informs you that the daily transfers take too long and have asked you to fix the problem. You want to maximize transfer speeds. Which action should you take?

A) Increase your network bandwidth from your datacenter to GCP.
B) Increase the size of the Google Persistent Disk on your server.
C) Increase the CPU size on your server.
D) Increase your network bandwidth from Compute Engine to Cloud Storage.

4. What is the general recommendation when designing your row keys for a Cloud Bigtable schema?

A) Keep your row key reasonably short
B) Keep the row keep as an 8 bit integer
C) Include multiple time series values within the row key
D) Keep your row key as long as the field permits

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

Solutions: