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Google Data Engineer Sample Questions:

1. You need to choose a database for a new project that has the following requirements:
Fully managed
Able to automatically scale up
Transactionally consistent
Able to scale up to 6 TB
Able to be queried using SQL Which database do you choose?

A) Cloud SQL
B) Cloud Spanner
C) Cloud Datastore
D) Cloud Bigtable

2. You have enabled the free integration between Firebase Analytics and Google BigQuery. Firebase now automatically creates a new table daily in BigQuery in the format app_events_YYYYMMDD. You want to query all of the tables for the past 30 days in legacy SQL. What should you do?

A) Use WHERE date BETWEEN YYYY-MM-DD AND YYYY-MM-DD
B) Use the TABLE_DATE_RANGE function
C) Use SELECT IF.(date >= YYYY-MM-DD AND date <= YYYY-MM-DD
D) Use the WHERE_PARTITIONTIME pseudo column

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

4. 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 writes to a Cloud SQL database to store the dat
B) Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.
C) 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.
D) 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.
E) Create an application that provides an AP
F) Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.

5. You receive data files in CSV format monthly from a third party. You need to cleanse this data, but every third month the schema of the files changes. Your requirements for implementing these transformations include:
Executing the transformations on a schedule
Enabling non-developer analysts to modify transformations
Providing a graphical tool for designing transformations
What should you do?

A) Use Apache Spark on Cloud Dataproc to infer the schema of the CSV file before creating a Dataframe.Then implement the transformations in Spark SQL before writing the data out to Cloud Storage and loading into BigQuery
B) Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard scheme
C) Merge the transformed tables together with a SQL query
D) Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformatio
E) Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis
F) The Python code should be stored in a revision control system and modified as the incoming data's schema changes

Solutions: