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PMLE Practice Question: Drag and drop the steps to set up a BigQuery ML…
Drag and drop the steps to set up a BigQuery ML linear regression model for forecasting in the correct order.
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
✓
Prepare training data → Create model → Evaluate model → Use model for predictions
For BigQuery ML linear regression, the correct order is: 1) Prepare training data by selecting and preprocessing features in a SQL query; 2) Create the model using `CREATE MODEL` with the training data; 3) Evaluate the model using `ML.EVALUATE` to check metrics like R²; 4) Use the model for predictions with `ML.PREDICT`. This sequence ensures the model is built on clean data, validated, and then applied.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Prepare training data → Create model → Evaluate model → Use model for predictions
Why this is correct
This is the correct order because you must first prepare your training data, then create the model using that data, evaluate its performance, and finally use it to make predictions.
- ✗
Prepare training data → Create model → Use model for predictions → Evaluate model
Why it's wrong here
This is incorrect because you should evaluate the model's performance before using it for predictions to ensure its accuracy and reliability.
- ✗
Create model → Prepare training data → Evaluate model → Use model for predictions
Why it's wrong here
This is incorrect because you cannot create a model without first preparing the training data, as the model requires input data for training.
- ✗
Prepare training data → Evaluate model → Create model → Use model for predictions
Why it's wrong here
This is incorrect because you must create the model before evaluating it; evaluation requires a trained model to assess.
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