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Google Data Engineer Sample Questions:

1. Which of these rules apply when you add preemptible workers to a Dataproc cluster (select 2 answers)?

A) Preemptible workers cannot use persistent disk.
B) If a preemptible worker is reclaimed, then a replacement worker must be added manually.
C) Preemptible workers cannot store data.
D) A Dataproc cluster cannot have only preemptible workers.

2. Flowlogistic's CEO wants to gain rapid insight into their customer base so his sales team can be better informed in the field. This team is not very technical, so they've purchased a visualization tool to simplify the creation of BigQuery reports. However, they've been overwhelmed by all the data in the table, and are spending a lot of money on queries trying to find the data they need. You want to solve their problem in the most cost-effective way. What should you do?

A) Export the data into a Google Sheet for virtualization.
B) Create identity and access management (IAM) roles on the appropriate columns, so only they appear in a query.
C) Create a view on the table to present to the virtualization tool.
D) Create an additional table with only the necessary columns.

3. The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster .

A) master node
B) application node
C) conditional node
D) worker node

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

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

Solutions: