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Google Data Engineer Sample Questions:

1. 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) Eliminate features that are highly correlated to the output labels.
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) Remove the features that have null values for more than 50% of the training records.

2. If you're running a performance test that depends upon Cloud Bigtable, all the choices except one below are recommended steps. Which is NOT a recommended step to follow?

A) Run your test for at least 10 minutes.
B) Use at least 300 GB of data.
C) Before you test, run a heavy pre-test for several minutes.
D) Do not use a production instance.

3. 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) Create a view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName.
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) Add a new column called FullName to the Users tabl

4. You are a retailer that wants to integrate your online sales capabilities with different in-home assistants, such as Google Home. You need to interpret customer voice commands and issue an order to the backend systems. Which solutions should you choose?

A) Cloud Natural Language API
B) Dialogflow Enterprise Edition
C) Cloud Speech-to-Text API
D) Cloud AutoML Natural Language

5. You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?

A) Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore
B) Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.
C) Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery
D) Store the data in Google Cloud Datastor
E) Store the data in a file in a regional Google Cloud Storage bucke
F) Load the data every 30 minutes into a new partitioned table in BigQuery.

Solutions: