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Google Data Engineer Sample Questions:

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

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

3. Which of these sources can you not load data into BigQuery from?

A) File upload
B) Google Cloud SQL
C) Google Drive
D) Google Cloud Storage

4. What are two methods that can be used to denormalize tables in BigQuery?

A) 1) Split table into multiple tables; 2) Use a partitioned table
B) 1) Join tables into one table; 2) Use nested repeated fields
C) 1) Use a partitioned table; 2) Join tables into one table
D) 1) Use nested repeated fields; 2) Use a partitioned table

5. Which of these is not a supported method of putting data into a partitioned table?

A) Use ORDER BY to put a table's rows into chronological order and then change the table's type to "Partitioned".
B) If you have existing data in a separate file for each day, then create a partitioned table and upload each file into the appropriate partition.
C) Create a partitioned table and stream new records to it every day.
D) Run a query to get the records for a specific day from an existing table and for the destination table, specify a partitioned table ending with the day in the format "$YYYYMMDD".

Solutions: