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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) Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values.
B) Use BigQuery to export the data for the table to a CSV fil
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) Create a view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName.

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

3. Suppose you have a dataset of images that are each labeled as to whether or not they contain a human face. To create a neural network that recognizes human faces in images using this labeled dataset, what approach would likely be the most effective?

A) Use K-means Clustering to detect faces in the pixels.
B) Use feature engineering to add features for eyes, noses, and mouths to the input data.
C) Use deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.
D) Build a neural network with an input layer of pixels, a hidden layer, and an output layer with two categories.

4. You need to create a data pipeline that copies time-series transaction data so that it can be queried from within BigQuery by your data science team for analysis. Every hour, thousands of transactions are updated with a new status. The size of the intitial dataset is 1.5 PB, and it will grow by 3 TB per day. The data is heavily structured, and your data science team will build machine learning models based on this dat a. You want to maximize performance and usability for your data science team. Which two strategies should you adopt? Choose 2 answers.

A) Copy a daily snapshot of transaction data to Cloud Storage and store it as an Avro fil
B) Develop a data pipeline where status updates are appended to BigQuery instead of updated.
C) Denormalize the data as must as possible.
D) Preserve the structure of the data as much as possible.
E) Use BigQuery'ssupport for external data sources to query.
F) Use BigQuery UPDATE to further reduce the size of the dataset.

5. You need to deploy additional dependencies to all of a Cloud Dataproc cluster at startup using an existing initialization action. Company security policies require that Cloud Dataproc nodes do not have access to the Internet so public initialization actions cannot fetch resources. What should you do?

A) Use an SSH tunnel to give the Cloud Dataproc cluster access to the Internet
B) Copy all dependencies to a Cloud Storage bucket within your VPC security perimeter
C) Use Resource Manager to add the service account used by the Cloud Dataproc cluster to the Network User role
D) Deploy the Cloud SQL Proxy on the Cloud Dataproc master

Solutions: