Objective 2.3
ADPDefine, train, evaluate, and use ML models
Objective 2.3 sits in Data Analysis and Presentation, which carries 27% of the Associate Data Practitioner exam. The questions below are original, written from the official objective title above, and each explanation cites the Google Cloud page it rests on.
Objective title verbatim from the official objectives. Google Cloud exam page ↗
A worked example
Shown solved, with the whole explanation open: this is what every question here carries.
A data analyst who knows SQL but not Python needs to build and evaluate ML models without moving data out of the warehouse. Which product fits?
Correct.
Concept
In-warehouse machine learning lets SQL users train models where the data already lives. This removes data movement and the need for a separate ML programming language.
Why B
BigQuery ML lets you create and run models using SQL, keeping data in the warehouse.
Source
Google Cloud, Introduction to ML in BigQuery, checked August 2026BigQuery ML lets you create and run machine learning (ML) models by using either GoogleSQL queries or the Google Cloud console.
Now you: objective 2.3 questions
No account needed. The explanation opens when you answer.
Sample question 1 of 3
An analyst plans an ML project and must order the standard stages correctly. Which sequence is standard?
Sample question 2 of 3
A SQL-focused analyst wants to access AI resources without programming in Python or Java. What does BigQuery ML let them use instead?
Sample question 3 of 3
You need to predict a continuous numeric value, such as expected order revenue, from historical features. Which BigQuery ML model type fits?
That’s 3 of the full Associate Data Practitioner bank.
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Continue practisingRead the sources
These are the official pages the questions above cite. Reading them is studying the objective from the primary source, which is what the explanations point you toward anyway.