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Google Data Engineer Sample Questions:

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

2. You are designing the database schema for a machine learning-based food ordering service that will predict what users want to eat. Here is some of the information you need to store:
The user profile: What the user likes and doesn't like to eat
The user account information: Name, address, preferred meal times
The order information: When orders are made, from where, to whom
The database will be used to store all the transactional data of the product. You want to optimize the data schem a. Which Google Cloud Platform product should you use?

A) Cloud SQL
B) Cloud Datastore
C) Cloud Bigtable
D) BigQuery

3. You are integrating one of your internal IT applications and Google BigQuery, so users can query BigQuery from the application's interface. You do not want individual users to authenticate to BigQuery and you do not want to give them access to the dataset. You need to securely access BigQuery from your IT application.
What should you do?

A) Create a service account and grant dataset access to that accoun
B) Store the username and password for that user in a file on the files system, and use those credentials to access the BigQuery dataset
C) Create groups for your users and give those groups access to the dataset
D) Integrate with a single sign-on (SSO) platform, and pass each user's credentials along with the query request
E) Use the service account's private key to access the dataset
F) Create a dummy user and grant dataset access to that use

4. You have historical data covering the last three years in BigQuery and a data pipeline that delivers new data to BigQuery daily. You have noticed that when the Data Science team runs a query filtered on a date column and limited to 30-90 days of data, the query scans the entire table. You also noticed that your bill is increasing more quickly than you expected. You want to resolve the issue as cost-effectively as possible while maintaining the ability to conduct SQL queries. What should you do?

A) Partition the tables by a column containing a TIMESTAMP or DATE Type.
B) Re-create the tables using DD
C) Write an Apache Beam pipeline that creates a BigQuery table per data
D) Recommend that the Data Science team export the table to a CSV file on Cloud Storage and use Cloud Datalab to explore the data by reading the files directly.
E) Recommend that the Data Science team use wildcards on the table name suffixes to select the data they need.
F) Modify your pipeline to maintain the last 30-90 days of data in one table and the longer history in a different table to minimize full table scans over the entire history.

5. You are building a model to predict whether or not it will rain on a given day. You have thousands of input features and want to see if you can improve training speed by removing some features while having a minimum effect on model accuracy. What can you do?

A) Remove the features that have null values for more than 50% of the training records.
B) Instead of feeding in each feature individually, average their values in batches of 3.
C) Combine highly co-dependent features into one representative feature.
D) Eliminate features that are highly correlated to the output labels.

Solutions: