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Google Data Engineer Sample Questions:

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

2. Each analytics team in your organization is running BigQuery jobs in their own projects. You want to enable each team to monitor slot usage within their projects. What should you do?

A) Create a Stackdriver Monitoring dashboard based on the BigQuery metric query/scanned_bytes
B) Create an aggregated log export at the organization level, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric
C) Create a log export for each project, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric
D) Create a Stackdriver Monitoring dashboard based on the BigQuery metric slots/allocated_for_project

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

4. Which row keys are likely to cause a disproportionate number of reads and/or writes on a particular node in a Bigtable cluster (select 2 answers)?

A) A stock symbol followed by a timestamp
B) A timestamp followed by a stock symbol
C) A sequential numeric ID
D) A non-sequential numeric ID

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