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Google Data Engineer Sample Questions:

1. 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) 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.
B) Modify the transformMapReduce jobs to apply sensor calibration before they do anything else.
C) Add sensor calibration data to the output of the ETL process, and document that all users need to apply sensor calibration themselves.
D) Introduce a new MapReduce job to apply sensor calibration to raw data, and ensure all other MapReduce jobs are chained after this.

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

3. You are creating a new pipeline in Google Cloud to stream IoT data from Cloud Pub/Sub through Cloud Dataflow to BigQuery. While previewing the data, you notice that roughly 2% of the data appears to be corrupt. You need to modify the Cloud Dataflow pipeline to filter out this corrupt data. What should you do?

A) Add a SideInput that returns a Boolean if the element is corrupt.
B) Add a GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.
C) Add a Partition transform in Cloud Dataflow to separate valid data from corrupt data.
D) Add a ParDo transform in Cloud Dataflow to discard corrupt elements.

4. 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 BigTable
B) Cloud SQL
C) BigQuery
D) Cloud Datastore

5. You used Cloud Dataprep to create a recipe on a sample of data in a BigQuery table. You want to reuse this recipe on a daily upload of data with the same schema, after the load job with variable execution time completes. What should you do?

A) Export the Cloud Dataprep job as a Cloud Dataflow template, and incorporate it into a Cloud Composer job.
B) Create an App Engine cron job to schedule the execution of the Cloud Dataprep job.
C) Export the recipe as a Cloud Dataprep template, and create a job in Cloud Scheduler.
D) Create a cron schedule in Cloud Dataprep.

Solutions: