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Google Data Engineer Sample Questions:

1. You are operating a Cloud Dataflow streaming pipeline. The pipeline aggregates events from a Cloud Pub/Sub subscription source, within a window, and sinks the resulting aggregation to a Cloud Storage bucket. The source has consistent throughput. You want to monitor an alert on behavior of the pipeline with Cloud Stackdriver to ensure that it is processing dat a. Which Stackdriver alerts should you create?

A) An alert based on an increase of subscription/num_undelivered_messages for the source and a rate of change decrease of instance/storage/used_bytes for the destination
B) An alert based on an increase of instance/storage/used_bytes for the source and a rate of change decrease of subscription/num_undelivered_messages for the destination
C) An alert based on a decrease of instance/storage/used_bytes for the source and a rate of change increase of subscription/num_undelivered_messages for the destination
D) An alert based on a decrease of subscription/num_undelivered_messages for the source and a rate of change increase of instance/storage/used_bytes for the destination

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

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

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

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

4. Which of the following job types are supported by Cloud Dataproc (select 3 answers)?

A) Pig
B) Spark
C) YARN
D) Hive

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) Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard scheme
C) Merge the transformed tables together with a SQL query
D) Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformatio
E) Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis
F) The Python code should be stored in a revision control system and modified as the incoming data's schema changes

Solutions: