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Google Data Engineer Sample Questions:

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

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

3. Which of these is not a supported method of putting data into a partitioned table?

A) Run a query to get the records for a specific day from an existing table and for the destination table, specify a partitioned table ending with the day in the format "$YYYYMMDD".
B) Create a partitioned table and stream new records to it every day.
C) Use ORDER BY to put a table's rows into chronological order and then change the table's type to "Partitioned".
D) If you have existing data in a separate file for each day, then create a partitioned table and upload each file into the appropriate partition.

4. MJTelco needs you to create a schema in Google Bigtable that will allow for the historical analysis of the last 2 years of records. Each record that comes in is sent every 15 minutes, and contains a unique identifier of the device and a data record. The most common query is for all the data for a given device for a given day. Which schema should you use?

A) Rowkey: data_pointColumn data: device_id, date
B) Rowkey: device_idColumn data: date, data_point
C) Rowkey: date#data_pointColumn data: device_id
D) Rowkey: dateColumn data: device_id, data_point
E) Rowkey: date#device_idColumn data: data_point

5. All Google Cloud Bigtable client requests go through a front-end server they are sent to a Cloud Bigtable node.

A) after
B) before
C) only if
D) once

Solutions: