PMLE Architecting Low-Code ML Solutions Practice Question
A data engineer wants to use BigQuery ML to train a model that predicts customer churn using a table with customer features and a label column. They want to use a deep neural network. Which model type should they specify?
⚠ Common exam trap
The trap is that candidates may confuse DNN_REGRESSOR with DNN_CLASSIFIER, but BigQuery ML uses different model types for regression vs. classification. DNN_CLASSIFIER is used for classification tasks like churn prediction, while DNN_REGRESSOR is for continuous values.
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
✓
DNN_CLASSIFIER
The DNN_CLASSIFIER model type in BigQuery ML is specifically designed for classification tasks using a deep neural network architecture. Since the problem is predicting customer churn (a binary classification problem) and the data engineer explicitly wants to use a deep neural network, DNN_CLASSIFIER is the appropriate choice.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
BOOSTED_TREE_CLASSIFIER
Why it's wrong here
BOOSTED_TREE_CLASSIFIER builds gradient-boosted decision trees, not a deep neural network, so it ignores the stated architecture requirement despite handling binary churn labels. It would be the right choice when tabular accuracy matters and interpretability or training speed outweighs neural-network depth.
- ✗
LOGISTIC_REG
Why it's wrong here
LOGISTIC_REG fits a linear logistic model, not a deep neural network, so it fails the explicit architecture requirement even though it correctly handles binary churn labels. It is the right choice for fast, interpretable baselines on tabular data where feature interactions are limited.
- ✓
DNN_CLASSIFIER
Why this is correct
DNN_CLASSIFIER fits because churn prediction is binary classification, and the stem specifies a deep neural network. BigQuery ML's DNN_CLASSIFIER trains a feed-forward neural network with a logistic output layer, satisfying both the label-column requirement and the requested architecture, unlike DNN_REGRESSOR, which predicts continuous values.
- ✗
DNN_REGRESSOR
Why it's wrong here
DNN_REGRESSOR outputs a continuous numeric value, whereas churn is a binary label requiring classification. The deep neural network architecture matches the requirement, but the regression task type is wrong; DNN_CLASSIFIER is the correct pairing of network and label type.
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