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Google Data Engineer Sample Questions:

1. You receive data files in CSV format monthly from a third party. You need to cleanse this data, but every third month the schema of the files changes. Your requirements for implementing these transformations include:
Executing the transformations on a schedule
Enabling non-developer analysts to modify transformations
Providing a graphical tool for designing transformations
What should you do?

A) Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard scheme
B) Merge the transformed tables together with a SQL query
C) The Python code should be stored in a revision control system and modified as the incoming data's schema changes
D) Use Apache Spark on Cloud Dataproc to infer the schema of the CSV file before creating a Dataframe.Then implement the transformations in Spark SQL before writing the data out to Cloud Storage and loading into BigQuery
E) Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis
F) Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformatio

2. 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 view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName.
B) Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values.
C) Add a new column called FullName to the Users tabl
D) 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.
E) 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.
F) Use BigQuery to export the data for the table to a CSV fil

3. You are developing an application on Google Cloud that will automatically generate subject labels for users' blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning. What should you do?

A) Build and train a text classification model using TensorFlo
B) Call the Cloud Natural Language API from your applicatio
C) Call the Cloud Natural Language API from your applicatio
D) Process the generated Sentiment Analysis as labels.
E) Call the model from your application and process the results as labels.
F) Deploy the model using a KubernetesEngine cluste
G) Deploy the model using Cloud Machine Learning Engin
H) Process the generated Entity Analysis as labels.
I) Build and train a text classification model using TensorFlo
J) Call the model from your application and process the results as labels.

4. Which is not a valid reason for poor Cloud Bigtable performance?

A) There are issues with the network connection.
B) The Cloud Bigtable cluster has too many nodes.
C) The workload isn't appropriate for Cloud Bigtable.
D) The table's schema is not designed correctly.

5. You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform. Your user base could grow exponentially, but you do not want to manage infrastructure scaling.
Which Google database service should you use?

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

Solutions: