Exam Associate Data Practitioner topic 1 question 1 discussion

Actual exam question from Google's Associate Data Practitioner

Question #: 1
Topic #: 1

[All Associate Data Practitioner Questions]

Your retail company wants to predict customer churn using historical purchase data stored in BigQuery. The dataset includes customer demographics, purchase history, and a label indicating whether the customer churned or not. You want to build a machine learning model to identify customers at risk of churning. You need to create and train a logistic regression model for predicting customer churn, using the customer_data table with the churned column as the target label. Which BigQuery ML query should you use?

A.
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B.
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C.
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D.
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Show Suggested Answer

Suggested Answer: B 🗳️

by trashbox at Jan. 22, 2025, 1:37 p.m.

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n2183712847

3 months, 2 weeks ago

Selected Answer: B

The best option is B. Option B is best because it correctly selects all columns as features except the churned column, and then explicitly designates the churned column as the label for BigQuery ML, which is required for training. Option A (SELECT *) is incorrect because while it includes the churned column, it doesn't explicitly define it as the label. Option C (SELECT * EXCEPT(churned)) is incorrect because it excludes the target churned column, which is essential for training a supervised model. Option D (SELECT churned as label) is incorrect because it only selects the label and excludes all feature columns needed for prediction. Therefore, Option B is the only query that correctly sets up the data for BigQuery ML logistic regression training.

upvoted 1 times

baviru

3 months ago

You can use SELECT * EXCEPT (foo) and SELECT foo AS bar. https://ln.run/dFRAn

upvoted 1 times

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trashbox

4 months, 3 weeks ago

Selected Answer: B

You can use SELECT * EXCEPT (foo) and SELECT foo AS bar.

upvoted 2 times

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