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Google GCP-DE Exam Syllabus Topics:

Google Data Engineer Sample Questions:

1. Your company maintains a hybrid deployment with GCP, where analytics are performed on your anonymized customer dat a. The data are imported to Cloud Storage from your data center through parallel uploads to a data transfer server running on GCP. Management informs you that the daily transfers take too long and have asked you to fix the problem. You want to maximize transfer speeds. Which action should you take?

A) Increase your network bandwidth from your datacenter to GCP.
B) Increase the size of the Google Persistent Disk on your server.
C) Increase the CPU size on your server.
D) Increase your network bandwidth from Compute Engine to Cloud Storage.

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

3. You need to deploy additional dependencies to all of a Cloud Dataproc cluster at startup using an existing initialization action. Company security policies require that Cloud Dataproc nodes do not have access to the Internet so public initialization actions cannot fetch resources. What should you do?

A) Use an SSH tunnel to give the Cloud Dataproc cluster access to the Internet
B) Deploy the Cloud SQL Proxy on the Cloud Dataproc master
C) Use Resource Manager to add the service account used by the Cloud Dataproc cluster to the Network User role
D) Copy all dependencies to a Cloud Storage bucket within your VPC security perimeter

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

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

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

Solutions: