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Google Data Engineer Sample Questions:

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

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

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

4. You are designing the database schema for a machine learning-based food ordering service that will predict what users want to eat. Here is some of the information you need to store:
The user profile: What the user likes and doesn't like to eat
The user account information: Name, address, preferred meal times
The order information: When orders are made, from where, to whom
The database will be used to store all the transactional data of the product. You want to optimize the data schem a. Which Google Cloud Platform product should you use?

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

5. What are all of the BigQuery operations that Google charges for?

A) Storage, queries, and exporting data
B) Storage, queries, and loading data from a file
C) Storage, queries, and streaming inserts
D) Queries and streaming inserts

Solutions: