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Google Data Engineer Sample Questions:

1. You have a data stored in BigQuery. The data in the BigQuery dataset must be highly available. You need to define a storage, backup, and recovery strategy of this data that minimizes cost. How should you configure the BigQuery table?

A) Set the BigQuery dataset to be regiona
B) In the event of an emergency, use the backup copy of the table.
C) In the event of an emergency, use a point-in-time snapshot to recover the data.
D) Create a scheduled query to make copies of the data to tables suffixed with the time of the backu
E) Set the BigQuery dataset to be multi-regional
F) Create a scheduled query to make copies of the data to tables suffixed with the time of the backup
G) In the event of an emergency, use the backup copy of the table.
H) In the event of an emergency, use a point-in-time snapshot to recover the data.
I) Set the BigQuery dataset to be multi-regional
J) Set the BigQuery dataset to be regiona

2. 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) BigQuery
B) Cloud Datastore
C) Cloud SQL
D) Cloud BigTable

3. If you're running a performance test that depends upon Cloud Bigtable, all the choices except one below are recommended steps. Which is NOT a recommended step to follow?

A) Do not use a production instance.
B) Use at least 300 GB of data.
C) Before you test, run a heavy pre-test for several minutes.
D) Run your test for at least 10 minutes.

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

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

Solutions: