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Google Data Engineer Sample Questions:

1. All Google Cloud Bigtable client requests go through a front-end server they are sent to a Cloud Bigtable node.

A) only if
B) after
C) once
D) before

2. Suppose you have a table that includes a nested column called "city" inside a column called "person", but when you try to submit the following query in BigQuery, it gives you an error.
SELECT person FROM `project1.example.table1` WHERE city = "London"
How would you correct the error?

A) Change "person" to "person.city".
B) Add ", UNNEST(person)" before the WHERE clause.
C) Add ", UNNEST(city)" before the WHERE clause.
D) Change "person" to "city.person".

3. 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 Datastore
B) Cloud Bigtable
C) BigQuery
D) Cloud SQL

4. 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) Eliminate features that are highly correlated to the output labels.
D) Combine highly co-dependent features into one representative feature.

5. 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) 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.
C) 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.
D) Recommend that the Data Science team use wildcards on the table name suffixes to select the data they need.
E) Re-create the tables using DD
F) Write an Apache Beam pipeline that creates a BigQuery table per data

Solutions: