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Google Data Engineer Sample Questions:

1. You work for a large fast food restaurant chain with over 400,000 employees. You store employee information in Google BigQuery in a Users table consisting of a FirstName field and a LastName field. A member of IT is building an application and asks you to modify the schema and data in BigQuery so the application can query a FullName field consisting of the value of the FirstName field concatenated with a space, followed by the value of the LastName field for each employee. How can you make that data available while minimizing cost?

A) Create a Google Cloud Dataflow job that queries BigQuery for the entire Users table, concatenates the FirstName value and LastName value for each user, and loads the proper values for FirstName, LastName, and FullName into a new table in BigQuery.
B) Create a Google Cloud Dataproc job to process the CSV file and output a new CSV file containing the proper values for FirstName, LastName and FullNam G. Run a BigQuery load job to load the new CSV file into BigQuery.
C) Add a new column called FullName to the Users tabl
D) Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values.
E) Use BigQuery to export the data for the table to a CSV fil
F) Create a view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName.

2. Cloud Dataproc is a managed Apache Hadoop and Apache service.

A) Spark
B) Fire
C) Ignite
D) Blaze

3. Which of the following job types are supported by Cloud Dataproc (select 3 answers)?

A) Spark
B) Hive
C) YARN
D) Pig

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

5. You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally. You also want to optimize data for range queries on nonkey columns. What should you do?

A) Use Cloud SQL for storag
B) Add secondary indexes to support query patterns.
C) Use Cloud Dataflow to transform data to support query patterns.
D) Use Cloud Spanner for storag
E) Use Cloud SQL for storag
F) Use Cloud Dataflow to transform data to support query patterns.
G) Add secondary indexes to support query patterns.
H) Use Cloud Spanner for storag

Solutions: