Practice Test 2 | Google Cloud Certified Professional Data Engineer | Dumps | Mock Test
Data analysts in your company use BigQuery as their structured database supporting SQL for querying. Recently, they started to study about machine learning and what possibilities they can achieve if they can apply some machine learning algorithms such as binary classification on existing data stored in BigQuery for customer behavior prediction. You are to assist preparing the required tools and resources for data analysts to apply machine learning algorithms on data. What should you do?
A. Launch a Cloud Compute Engine virtual machine. Install TensorFlow within the virtual machine. Allow the machine to access data in BigQuery. Build and train the model. Write the model’s output to BigQuery.
B. Use BigQuery ML by building the binary classification model by specifying the model type and labels when creating the model.
C. Use Apache Spark MLLib with Dataproc to build the model. Use Dataproc-BigQuery connector to allow the cluster reading data.
D. Use Cloud ML Engine to build the classification model(s) using TensorFlow, allowing the model’s service account permission to read the data from BigQuery.
Answer: B.
Description:
BigQuery ML enables users to create and execute machine learning models in BigQuery using standard SQL queries. BigQuery ML democratizes machine learning by enabling SQL practitioners to build models using existing SQL tools and skills. BigQuery ML increases development speed by eliminating the need to move data.
BigQuery ML empowers data analysts to use machine learning through existing SQL tools and skills. Analysts can use BigQuery ML to build and evaluate ML models in BigQuery. Analysts no longer need to export small amounts of data to a spreadsheets or other applications, and analysts no longer need to wait for limited resources from a data science team.
Source(s):
Introduction to BigQuery ML:
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