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

2. 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.

3. MJTelco is building a custom interface to share dat
a. They have these requirements:
They need to do aggregations over their petabyte-scale datasets.
They need to scan specific time range rows with a very fast response time (milliseconds). Which combination of Google Cloud Platform products should you recommend?

A) Cloud Datastore and Cloud Bigtable
B) BigQuery and Cloud Bigtable
C) Cloud Bigtable and Cloud SQL
D) BigQuery and Cloud Storage

4. 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) Cloud Scheduler
C) cron
D) Workflow Templates on Cloud Dataproc

5. You receive data files in CSV format monthly from a third party. You need to cleanse this data, but every third month the schema of the files changes. Your requirements for implementing these transformations include:
Executing the transformations on a schedule
Enabling non-developer analysts to modify transformations
Providing a graphical tool for designing transformations
What should you do?

A) Use Apache Spark on Cloud Dataproc to infer the schema of the CSV file before creating a Dataframe.Then implement the transformations in Spark SQL before writing the data out to Cloud Storage and loading into BigQuery
B) The Python code should be stored in a revision control system and modified as the incoming data's schema changes
C) Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis
D) Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard scheme
E) Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformatio
F) Merge the transformed tables together with a SQL query

Solutions: