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Google Data Engineer Sample Questions:

1. 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, but reduce your dataset twice.
C) Use Cloud Vision API by providing custom labels as recognition hints.
D) Use Cloud Vision AutoML with the existing dataset.

2. You are responsible for writing your company's ETL pipelines to run on an Apache Hadoop cluster. The pipeline will require some checkpointing and splitting pipelines. Which method should you use to write the pipelines?

A) Java using MapReduce
B) PigLatin using Pig
C) Python using MapReduce
D) HiveQL using Hive

3. You work for a mid-sized enterprise that needs to move its operational system transaction data from an on-premises database to GCP. The database is about 20 TB in size. Which database should you choose?

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

4. When using Cloud Dataproc clusters, you can access the YARN web interface by configuring a browser to connect through a proxy.

A) HTTP
B) VPN
C) SOCKS
D) HTTPS

5. 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 Bigtable
B) Cloud Datastore
C) Cloud SQL
D) BigQuery

Solutions: