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Google Data Engineer Sample Questions:

1. Suppose you have a dataset of images that are each labeled as to whether or not they contain a human face. To create a neural network that recognizes human faces in images using this labeled dataset, what approach would likely be the most effective?

A) Use K-means Clustering to detect faces in the pixels.
B) Use deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.
C) Use feature engineering to add features for eyes, noses, and mouths to the input data.
D) Build a neural network with an input layer of pixels, a hidden layer, and an output layer with two categories.

2. 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

3. 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 a view on the table to present to the virtualization tool.
C) Create an additional table with only the necessary columns.
D) Create identity and access management (IAM) roles on the appropriate columns, so only they appear in a query.

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

5. You are planning to use Google's Dataflow SDK to analyze customer data such as displayed below. Your project requirement is to extract only the customer name Passing Certification Exams Made Easy visit - https://www.2PassEasy.com from the data source and then write to an output PCollection.
Tom,555 X street Tim,553 Y street Sam, 111 Z street
Which operation is best suited for the above data processing requirement?

A) Sink API
B) ParDo
C) Data extraction
D) Source API

Solutions: