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Google Data Engineer Sample Questions:

1. You want to automate execution of a multi-step data pipeline running on Google Cloud. The pipeline includes Cloud Dataproc and Cloud Dataflow jobs that have multiple dependencies on each other. You want to use managed services where possible, and the pipeline will run every day. Which tool should you use?

A) Cloud Composer
B) cron
C) Workflow Templates on Cloud Dataproc
D) Cloud Scheduler

2. Which of the following is NOT one of the three main types of triggers that Dataflow supports?

A) Trigger that is a combination of other triggers
B) Trigger based on time
C) Trigger based on element count
D) Trigger based on element size in bytes

3. Which row keys are likely to cause a disproportionate number of reads and/or writes on a particular node in a Bigtable cluster (select 2 answers)?

A) A timestamp followed by a stock symbol
B) A non-sequential numeric ID
C) A stock symbol followed by a timestamp
D) A sequential numeric ID

4. You are creating a new pipeline in Google Cloud to stream IoT data from Cloud Pub/Sub through Cloud Dataflow to BigQuery. While previewing the data, you notice that roughly 2% of the data appears to be corrupt. You need to modify the Cloud Dataflow pipeline to filter out this corrupt data. What should you do?

A) Add a Partition transform in Cloud Dataflow to separate valid data from corrupt data.
B) Add a GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.
C) Add a ParDo transform in Cloud Dataflow to discard corrupt elements.
D) Add a SideInput that returns a Boolean if the element is corrupt.

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

Solutions: