Google GCP-DE - Data Engineer

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Google GCP-DE Exam Syllabus Topics:

Google Data Engineer Sample Questions:

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

2. You have historical data covering the last three years in BigQuery and a data pipeline that delivers new data to BigQuery daily. You have noticed that when the Data Science team runs a query filtered on a date column and limited to 30-90 days of data, the query scans the entire table. You also noticed that your bill is increasing more quickly than you expected. You want to resolve the issue as cost-effectively as possible while maintaining the ability to conduct SQL queries. What should you do?

A) Partition the tables by a column containing a TIMESTAMP or DATE Type.
B) Write an Apache Beam pipeline that creates a BigQuery table per data
C) Recommend that the Data Science team use wildcards on the table name suffixes to select the data they need.
D) Recommend that the Data Science team export the table to a CSV file on Cloud Storage and use Cloud Datalab to explore the data by reading the files directly.
E) Re-create the tables using DD
F) Modify your pipeline to maintain the last 30-90 days of data in one table and the longer history in a different table to minimize full table scans over the entire history.

3. When using Cloud Dataproc clusters, you can access the YARN web interface by configuring a browser to connect through a proxy.

A) VPN
B) SOCKS
C) HTTPS
D) HTTP

4. 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 Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis
B) Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformatio
C) 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
D) Merge the transformed tables together with a SQL query
E) The Python code should be stored in a revision control system and modified as the incoming data's schema changes
F) Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard scheme

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

Solutions: