Practice Test 3 | Google Cloud Certified Professional Data Engineer | Dumps | Mock Test

Data science team decided to use BigQuery ML for their experimental predictive model scanning session logs generated from the web application that the development team has built and currently maintaining. To use the predictive model in BigQuery, data scientists run the following query to get predictions for the period from July 2017 to August 2018:

SELECT *

FROM ML.EVALUATE(MODEL `bqml.predictive_model`, (

SELECT *

FROM `ml-project.predictive_data_table.sessions_*`

WHERE _TABLE_SUFFIX BETWEEN ‘20170701’ AND ‘20180801’))

As a data engineer, you are asked to build a solution which runs this query every 24 hours, scanning session logs for the last 30 days. Results should be written in Google Storage for several data ingestion and processing tools are able to read prediction output. What would you do?

A. Write a script to run the query in BigQuery, get the results and write them to Google Storage. Deploy the script using Docker to Compute Engine VM instance.
B. Schedule the query to run every 24 hours and export results to Google Storage using BigQuery’s scheduled queries tool.
C. Use Dataproc to run the query on BigQuery. Write the results returned to Google Storage.
D. Build a Dataflow pipeline with scheduler to run the query in BigQuery and export the results to Google Storage.