Practice Test 1 | Google Cloud Certified Professional Data Engineer | Dumps | Mock Test
An online retail company uses BigQuery to store its session logs generated by visitors browsing its website. The marketing team wants to know who are the visitors potential to buy from the online store. This is to focus their marketing efforts on those visitors instead of mass marketing over 100,000 visitors a month. You want to build a logistic regression model to predict visitors willing to buy from the company’s products using the logs stored in BigQuery. What should you do?
A. Use Vertex AI to build a model using TensorFlow allowing permission to the engine’s service account to read from BigQuery.
B. Use Dataproc to build a logistic regression model using Spark MLLib. Install Dataproc-BigQuery connector for the cluster to access session logs.
C. Use BigQuery ML by building a model and specifying the model type as logistic regression and the labels.
D. After building a logistic regression model using TensorFlow, export session logs data from BigQuery to Google Storage. Deploy the cluster to Vertex AI and allow its service account to read data from Google Storage.
Correct Answer: C
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 spreadsheets or other applications, and analysts no longer need to wait for limited resources from a data science team.
Option A is incorrect: Vertex AI is used to deploy models. It does not help to build the models.
Option B & D are incorrect: These approaches are not required if BigQuery ML is adequate for the scenario.
Source(s):
Introduction to BigQuery ML:
BigQuery ML – Machine Learning using SQL in BigQuery:
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