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Google Data Engineer Sample Questions:

1. Cloud Dataproc is a managed Apache Hadoop and Apache service.

A) Spark
B) Blaze
C) Fire
D) Ignite

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

3. An online retailer has built their current application on Google App Engine. A new initiative at the company mandates that they extend their application to allow their customers to transact directly via the application.
They need to manage their shopping transactions and analyze combined data from multiple datasets using a business intelligence (BI) tool. They want to use only a single database for this purpose. Which Google Cloud database should they choose?

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

4. By default, which of the following windowing behavior does Dataflow apply to unbounded data sets?

A) Windows at every 100 MB of data
B) Windows at every 10 minutes
C) Windows at every 1 minute
D) Single, Global Window

5. You work for a manufacturing company that sources up to 750 different components, each from a different supplier. You've collected a labeled dataset that has on average 1000 examples for each unique component. Your team wants to implement an app to help warehouse workers recognize incoming components based on a photo of the component. You want to implement the first working version of this app (as Proof-Of-Concept) within a few working days. What should you do?

A) Train your own image recognition model leveraging transfer learning techniques.
B) Use Cloud Vision AutoML with the existing dataset.
C) Use Cloud Vision AutoML, but reduce your dataset twice.
D) Use Cloud Vision API by providing custom labels as recognition hints.

Solutions: