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Google GCP-DE Exam Syllabus Topics:

Google Data Engineer Sample Questions:

Question 1

You want to migrate an on-premises Hadoop system to Cloud Dataproc. Hive is the primary tool in use, and the data format is Optimized Row Columnar (ORC). All ORC files have been successfully copied to a Cloud Storage bucket. You need to replicate some data to the cluster's local Hadoop Distributed File System (HDFS) to maximize performance. What are two ways to start using Hive in Cloud Dataproc? (Choose two.)

A. Replicate external Hive tables to the native ones.
B. Mount the Hive tables from HDFS.
C. Leverage BigQuery connector for Hadoop to mount the BigQuery tables as external Hive table
D. Then run the Hadoop utility to copy them do HDF
E. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to any node of the Dataproc cluste
F. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to the master node of the Dataproc cluste
G. Leverage Cloud Storage connector for Hadoop to mount the ORC files as external Hive table
H. Mount the Hive tables locally.
I. Mount the Hive tables locally.
J. Load the ORC files into BigQuer
K. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to HDF
L. Replicate external Hive tables to the native ones.

Question 2

An online retailer has built their current application on Google App Engine. A new initiative at the company mandates that they extend their application to allow their customers to transact directly via the application.
They need to manage their shopping transactions and analyze combined data from multiple datasets using a business intelligence (BI) tool. They want to use only a single database for this purpose. Which Google Cloud database should they choose?

A. Cloud Datastore
B. Cloud BigTable
C. BigQuery
D. Cloud SQL

Question 3

When using Cloud Dataproc clusters, you can access the YARN web interface by configuring a browser to connect through a proxy.

A. VPN
B. HTTPS
C. HTTP
D. SOCKS

Question 4

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. The Python code should be stored in a revision control system and modified as the incoming data's schema changes
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. 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

Question 5

You launched a new gaming app almost three years ago. You have been uploading log files from the previous day to a separate Google BigQuery table with the table name format LOGS_yyyymmdd. You have been using table wildcard functions to generate daily and monthly reports for all time ranges. Recently, you discovered that some queries that cover long date ranges are exceeding the limit of 1,000 tables and failing. How can you resolve this issue?

A. Convert the sharded tables into a single partitioned table
B. Create separate views to cover each month, and query from these views
C. Convert all daily log tables into date-partitioned tables
D. Enable query caching so you can cache data from previous months

Solutions: