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Google Data Engineer Sample Questions:

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

2. 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 a decrease of instance/storage/used_bytes for the source and a rate of change increase of subscription/num_undelivered_messages for the destination
B) 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
C) 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
D) 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

3. Which Cloud Dataflow / Beam feature should you use to aggregate data in an unbounded data source every hour based on the time when the data entered the pipeline?

A) An hourly watermark
B) A processing time trigger
C) An event time trigger
D) The with Allowed Lateness method

4. What Dataflow concept determines when a Window's contents should be output based on certain criteria being met?

A) Triggers
B) Windows
C) OutputCriteria
D) Sessions

5. When a Cloud Bigtable node fails, is lost.

A) the last transaction
B) all data
C) the time dimension
D) no data

Solutions: