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Google Data Engineer Sample Questions:

1. 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) Cloud SQL
B) Cloud Bigtable
C) Cloud Datastore
D) BigQuery

2. You are building a new application that you need to collect data from in a scalable way. Data arrives continuously from the application throughout the day, and you expect to generate approximately 150 GB of JSON data per day by the end of the year. Your requirements are: Decoupling producer from consumer Space and cost-efficient storage of the raw ingested data, which is to be stored indefinitely Near real-time SQL query Maintain at least 2 years of historical data, which will be queried with SQ Which pipeline should you use to meet these requirements?

A) Create an application that publishes events to Cloud Pub/Sub, and create Spark jobs on Cloud Dataproc to convert the JSON data to Avro format, stored on HDFS on Persistent Disk.
B) Create an application that writes to a Cloud SQL database to store the dat
C) Create an application that provides an AP
D) Create an application that publishes events to Cloud Pub/Sub, and create a Cloud Dataflow pipeline that transforms the JSON event payloads to Avro, writing the data to Cloud Storage and BigQuery.
E) Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.
F) Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.

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

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

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

5. You want to automate execution of a multi-step data pipeline running on Google Cloud. The pipeline includes Cloud Dataproc and Cloud Dataflow jobs that have multiple dependencies on each other. You want to use managed services where possible, and the pipeline will run every day. Which tool should you use?

A) cron
B) Cloud Composer
C) Workflow Templates on Cloud Dataproc
D) Cloud Scheduler

Solutions: