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Google Data Engineer Sample Questions:

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

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

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

3. Which of these sources can you not load data into BigQuery from?

A) File upload
B) Google Cloud Storage
C) Google Drive
D) Google Cloud SQL

4. 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) Enable query caching so you can cache data from previous months
D) Convert all daily log tables into date-partitioned tables

5. You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?

A) Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore
B) Load the data every 30 minutes into a new partitioned table in BigQuery.
C) Store the data in a file in a regional Google Cloud Storage bucke
D) Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.
E) Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery
F) Store the data in Google Cloud Datastor

Solutions: