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Google GCP-DE Exam Syllabus Topics:

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

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

3. You architect a system to analyze seismic dat
a. Your extract, transform, and load (ETL) process runs as a series of MapReduce jobs on an Apache Hadoop cluster. The ETL process takes days to process a data set because some steps are computationally expensive. Then you discover that a sensor calibration step has been omitted. How should you change your ETL process to carry out sensor calibration systematically in the future?

A) Introduce a new MapReduce job to apply sensor calibration to raw data, and ensure all other MapReduce jobs are chained after this.
B) Add sensor calibration data to the output of the ETL process, and document that all users need to apply sensor calibration themselves.
C) Modify the transformMapReduce jobs to apply sensor calibration before they do anything else.
D) Develop an algorithm through simulation to predict variance of data output from the last MapReduce job based on calibration factors, and apply the correction to all data.

4. You are responsible for writing your company's ETL pipelines to run on an Apache Hadoop cluster. The pipeline will require some checkpointing and splitting pipelines. Which method should you use to write the pipelines?

A) Python using MapReduce
B) HiveQL using Hive
C) Java using MapReduce
D) PigLatin using Pig

5. You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform. Your user base could grow exponentially, but you do not want to manage infrastructure scaling.
Which Google database service should you use?

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

Solutions: