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Google Data Engineer Sample Questions:

1. You are building a new data pipeline to share data between two different types of applications: jobs generators and job runners. Your solution must scale to accommodate increases in usage and must accommodate the addition of new applications without negatively affecting the performance of existing ones. What should you do?

A) Create a table on Cloud Spanner, and insert and delete rows with the job information
B) Create a table on Cloud SQL, and insert and delete rows with the job information
C) Use a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them
D) Create an API using App Engine to receive and send messages to the applications

2. Which of the following is NOT true about Dataflow pipelines?

A) Dataflow pipelines can be programmed in Java
B) Dataflow pipelines can consume data from other Google Cloud services
C) Dataflow pipelines use a unified programming model, so can work both with streaming and batch data sources
D) Dataflow pipelines are tied to Dataflow, and cannot be run on any other runner

3. You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally. You also want to optimize data for range queries on nonkey columns. What should you do?

A) Use Cloud Spanner for storag
B) Use Cloud SQL for storag
C) Add secondary indexes to support query patterns.
D) Use Cloud Dataflow to transform data to support query patterns.
E) Use Cloud SQL for storag
F) Use Cloud Spanner for storag
G) Add secondary indexes to support query patterns.
H) Use Cloud Dataflow to transform data to support query patterns.

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

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

5. You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?

A) Store the data in Google Cloud Datastor
B) Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore
C) Store the data in a file in a regional Google Cloud Storage bucke
D) Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.
E) Load the data every 30 minutes into a new partitioned table in BigQuery.
F) Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery

Solutions: