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PMLE Architecting Low-Code ML Solutions Practice Question

A marketing team wants to build a model that predicts whether a customer will click on an ad, using a dataset in BigQuery. They have limited ML expertise and want to avoid writing complex code. They decide to use BigQuery ML with a logistic regression model. Which SQL statement should they use to create the model?

⚠ Common exam trap

Many exam-takers confuse linear regression with logistic regression for binary classification tasks.

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

✓

CREATE MODEL `project.dataset.model` OPTIONS(model_type='logistic_reg', input_label_cols=['clicked']) AS SELECT * FROM `project.dataset.training_data`

The correct SQL statement creates a logistic regression model in BigQuery ML with the proper model type and label column specification. Logistic regression is ideal for binary classification tasks such as predicting ad clicks. The input_label_cols option identifies the target variable, and the SELECT statement supplies the training data. This approach allows users with limited ML expertise to build a model using only SQL.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • ✓

    CREATE MODEL `project.dataset.model` OPTIONS(model_type='logistic_reg', input_label_cols=['clicked']) AS SELECT * FROM `project.dataset.training_data`

    Why this is correct

    This statement correctly creates a logistic regression model in BigQuery ML. It specifies the model type as logistic_reg and identifies the label column as 'clicked' using the input_label_cols option. The SELECT statement provides the training data, which includes both features and the label. BigQuery ML will automatically use all other columns as features, making it suitable for users with limited ML expertise.

  • ✗

    CREATE MODEL `project.dataset.model` OPTIONS(model_type='linear_reg', input_label_cols=['clicked']) AS SELECT * FROM `project.dataset.training_data`

    Why it's wrong here

    This statement uses linear_reg as the model type, which is designed for regression tasks predicting continuous values, not binary classification. The goal is to predict whether a customer will click (a binary outcome), so logistic regression is appropriate. Using linear regression would produce continuous predictions that are not directly interpretable as probabilities, leading to poor performance for classification.

  • ✗

    CREATE MODEL `project.dataset.model` OPTIONS(model_type='logistic_reg') AS SELECT * FROM `project.dataset.training_data`

    Why it's wrong here

    This statement attempts to create a logistic regression model without specifying the input features and label column. BigQuery ML requires the SELECT statement to provide the feature columns and the label column, typically using an explicit list or EXCEPT clause. Without that, the model cannot be trained correctly because it does not know which column is the target. Therefore, it is not the correct approach.

  • ✗

    CREATE MODEL `project.dataset.model` OPTIONS(model_type='kmeans', input_label_cols=['clicked']) AS SELECT * FROM `project.dataset.training_data`

    Why it's wrong here

    This statement attempts to create a k-means clustering model, which is an unsupervised learning algorithm used for grouping data points, not for predicting a binary label. The input_label_cols option is not applicable to k-means because it does not use a label. For predicting ad clicks, a supervised classification model like logistic regression is required, making this option incorrect.

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Written and reviewed by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

Last reviewed September 2026 · checked against the official Google Cloud exam blueprint

This PMLE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PMLE exam.