Google Data Engineer Sample Questions:

1. You have a data pipeline with a Cloud Dataflow job that aggregates and writes time series metrics to Cloud Bigtable. This data feeds a dashboard used by thousands of users across the organization. You need to support additional concurrent users and reduce the amount of time required to write the dat a. Which two actions should you take? (Choose two.)

A) Increase the number of nodes in the Cloud Bigtable cluster
B) Modify your Cloud Dataflow pipeline to use the Flatten transform before writing to Cloud Bigtable
C) Configure your Cloud Dataflow pipeline to use local execution
D) Increase the maximum number of Cloud Dataflow workers by setting maxNumWorkers in PipelineOptions
E) Modify your Cloud Dataflow pipeline to use the CoGroupByKey transform before writing to Cloud Bigtable

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

A) Use ORDER BY to put a table's rows into chronological order and then change the table's type to "Partitioned".
B) 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".
C) 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.
D) Create a partitioned table and stream new records to it every day.

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

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

4. Which is not a valid reason for poor Cloud Bigtable performance?

A) The table's schema is not designed correctly.
B) The workload isn't appropriate for Cloud Bigtable.
C) There are issues with the network connection.
D) The Cloud Bigtable cluster has too many nodes.

5. Your company produces 20,000 files every hour. Each data file is formatted as a comma separated values (CSV) file that is less than 4 KB. All files must be ingested on Google Cloud Platform before they can be processed. Your company site has a 200 ms latency to Google Cloud, and your Internet connection bandwidth is limited as 50 Mbps. You currently deploy a secure FTP (SFTP) server on a virtual machine in Google Compute Engine as the data ingestion point. A local SFTP client runs on a dedicated machine to transmit the CSV files as is. The goal is to make reports with data from the previous day available to the executives by 10:00 a.m. each day. This design is barely able to keep up with the current volume, even though the bandwidth utilization is rather low.
You are told that due to seasonality, your company expects the number of files to double for the next three months. Which two actions should you take? (choose two.)

A) Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.
B) Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.
C) Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.
D) Create an S3-compatible storage endpoint in your network, and use Google Cloud Storage Transfer Service to transfer on-premices data to the designated storage bucket.
E) Assemble 1,000 files into a tape archive (TAR) fil
F) Introduce data compression for each file to increase the rate file of file transfer.

Solutions: