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CLOUD-DIGITAL-LEADER · Question #364

A vacation home rental organization wants to predict the popularity of properties in their upcoming busy season. They do not have a data science team, and want to use their in-house database…

The correct answer is C. Use BigQuery ML and create models using SQL. BigQuery ML enables users to create and train machine learning models directly using SQL queries, making it ideal for teams with database administration skills but no data science expertise.

Leveraging Google Cloud Machine Learning Services

Question

A vacation home rental organization wants to predict the popularity of properties in their upcoming busy season. They do not have a data science team, and want to use their in-house database administration skills to create a machine learning model. What should the organization do?

Options

  • AUse custom training in Vertex AI with TensorFlow.
  • BIntegrate pre-trained APIs into their application.
  • CUse BigQuery ML and create models using SQL.
  • DBuild a model in AutoML using labeled data.

How the community answered

(30 responses)
  • A
    7% (2)
  • B
    7% (2)
  • C
    83% (25)
  • D
    3% (1)

Why each option

BigQuery ML enables users to create and train machine learning models directly using SQL queries, making it ideal for teams with database administration skills but no data science expertise.

AUse custom training in Vertex AI with TensorFlow.

Custom training with TensorFlow requires significant data science expertise in Python and ML frameworks, which the organization explicitly does not have.

BIntegrate pre-trained APIs into their application.

Pre-trained APIs apply general models to common tasks like image recognition or translation but cannot be trained on the organization's specific property popularity data.

CUse BigQuery ML and create models using SQL.Correct

BigQuery ML allows users to create, train, evaluate, and deploy ML models using standard SQL syntax within BigQuery, eliminating the need for Python, TensorFlow, or data science expertise. Database administrators familiar with SQL can leverage their existing skills to build predictive models directly on data already stored in BigQuery. This aligns perfectly with the organization's in-house SQL and database administration capabilities.

DBuild a model in AutoML using labeled data.

AutoML requires labeled training data and a drag-and-drop workflow but does not leverage SQL or database administration skills, making it a poorer fit than BigQuery ML for this team.

Concept tested: BigQuery ML for SQL-based machine learning model creation

Source: https://cloud.google.com/bigquery/docs/bqml-introduction

Topics

#BigQuery ML#Machine Learning#Low-code ML#SQL

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