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Google Data Engineer Sample Questions:

1. Your company is loading comma-separated values (CSV) files into Google BigQuery. The data is fully imported successfully; however, the imported data is not matching byte-to-byte to the source file. What is the most likely cause of this problem?

A) The CSV data loaded in BigQuery is not using BigQuery's default encoding.
B) The CSV data has not gone through an ETL phase before loading into BigQuery.
C) The CSV data loaded in BigQuery is not flagged as CSV.
D) The CSV data has invalid rows that were skipped on import.

2. You are a retailer that wants to integrate your online sales capabilities with different in-home assistants, such as Google Home. You need to interpret customer voice commands and issue an order to the backend systems. Which solutions should you choose?

A) Cloud Speech-to-Text API
B) Dialogflow Enterprise Edition
C) Cloud Natural Language API
D) Cloud AutoML Natural Language

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

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

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) Store the data in Google Cloud Datastor
B) Store the data in a file in a regional Google Cloud Storage bucke
C) Load the data every 30 minutes into a new partitioned table in BigQuery.
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) Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore

Solutions: