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Google Data Engineer Sample Questions:

1. You are selecting services to write and transform JSON messages from Cloud Pub/Sub to BigQuery for a data pipeline on Google Cloud. You want to minimize service costs. You also want to monitor and accommodate input data volume that will vary in size with minimal manual intervention. What should you do?

A) Use Cloud Dataproc to run your transformation
B) Configure the job to use non-default Compute Engine machine types when needed.
C) Use the default autoscaling setting for worker instances.
D) Monitor the total execution time for a sampling of job
E) Use Cloud Dataproc to run your transformation
F) Use Cloud Dataflow to run your transformation
G) Monitor the job system lag with Stackdrive
H) Locate the bottleneck and adjust cluster resources.
I) Use Cloud Dataflow to run your transformation
J) Monitor CPU utilization for the cluste
K) Resize the number of worker nodes in your cluster via the command line.
L) Use the diagnose command to generate an operational output archiv

2. Which of the following is NOT true about Dataflow pipelines?

A) Dataflow pipelines can be programmed in Java
B) Dataflow pipelines can consume data from other Google Cloud services
C) Dataflow pipelines are tied to Dataflow, and cannot be run on any other runner
D) Dataflow pipelines use a unified programming model, so can work both with streaming and batch data sources

3. 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) Deploy the Cloud SQL Proxy on the Cloud Dataproc master
D) Use Resource Manager to add the service account used by the Cloud Dataproc cluster to the Network User role

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

5. An online retailer has built their current application on Google App Engine. A new initiative at the company mandates that they extend their application to allow their customers to transact directly via the application.
They need to manage their shopping transactions and analyze combined data from multiple datasets using a business intelligence (BI) tool. They want to use only a single database for this purpose. Which Google Cloud database should they choose?

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

Solutions: