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Google Data Engineer Sample Questions:

1. You are integrating one of your internal IT applications and Google BigQuery, so users can query BigQuery from the application's interface. You do not want individual users to authenticate to BigQuery and you do not want to give them access to the dataset. You need to securely access BigQuery from your IT application.
What should you do?

A) Store the username and password for that user in a file on the files system, and use those credentials to access the BigQuery dataset
B) Create a dummy user and grant dataset access to that use
C) Create groups for your users and give those groups access to the dataset
D) Use the service account's private key to access the dataset
E) Integrate with a single sign-on (SSO) platform, and pass each user's credentials along with the query request
F) Create a service account and grant dataset access to that accoun

2. 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) Copy all dependencies to a Cloud Storage bucket within your VPC security perimeter
B) Use an SSH tunnel to give the Cloud Dataproc cluster access to the Internet
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

3. 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 BigTable
B) BigQuery
C) Cloud SQL
D) Cloud Datastore

4. You are running a pipeline in Cloud Dataflow that receives messages from a Cloud Pub/Sub topic and writes the results to a BigQuery dataset in the EU. Currently, your pipeline is located in europe-west4 and has a maximum of 3 workers, instance type n1-standard-1. You notice that during peak periods, your pipeline is struggling to process records in a timely fashion, when all 3 workers are at maximum CPU utilization. Which two actions can you take to increase performance of your pipeline? (Choose two.)

A) Create a temporary table in Cloud Bigtable that will act as a buffer for new data
B) Create a temporary table in Cloud Spanner that will act as a buffer for new data
C) Change the zone of your Cloud Dataflow pipeline to run in us-central1
D) Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Spanner to BigQuery
E) Increase the number of max workers
F) Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Bigtable to BigQuery
G) Use a larger instance type for your Cloud Dataflow workers

5. You have a data pipeline with a Cloud Dataflow job that aggregates and writes time series metrics to Cloud Bigtable. This data feeds a dashboard used by thousands of users across the organization. You need to support additional concurrent users and reduce the amount of time required to write the dat a. Which two actions should you take? (Choose two.)

A) Configure your Cloud Dataflow pipeline to use local execution
B) Increase the number of nodes in the Cloud Bigtable cluster
C) Modify your Cloud Dataflow pipeline to use the CoGroupByKey transform before writing to Cloud Bigtable
D) Modify your Cloud Dataflow pipeline to use the Flatten transform before writing to Cloud Bigtable
E) Increase the maximum number of Cloud Dataflow workers by setting maxNumWorkers in PipelineOptions

Solutions: