PMLE Architecting Low-Code ML Solutions Practice Question
A financial services firm wants to predict loan default risk using a dataset with 30,000 labeled examples and 25 numeric and categorical features. Their team includes SQL analysts but no Python developers, and they want to minimize operational overhead. They decide to use BigQuery ML. Which model type should they use to achieve the best predictive performance while keeping the solution low-code?
⚠ Common exam trap
The trap here is assuming that a simple linear model like logistic regression is sufficient for all classification tasks, when actually boosted trees often yield better performance on tabular data without added complexity.
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
✓
BOOSTED_TREE_CLASSIFIER
For a binary classification task with a moderate-sized tabular dataset and a low-code requirement, BigQuery ML's BOOSTED_TREE_CLASSIFIER offers an excellent balance of accuracy and ease of use. It automatically handles categorical features and non-linear relationships, often outperforming linear models. It requires only SQL to train and deploy, aligning with the team's skills and minimizing operational overhead.
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 this is correct
BOOSTED_TREE_CLASSIFIER is an ensemble model that often achieves higher accuracy on tabular data with non-linear relationships, such as loan default prediction. It is fully supported in BigQuery ML, requires only SQL to train, and automatically handles feature engineering like categorical encoding. With 30,000 examples, it has sufficient data to train effectively without overfitting, making it ideal for this low-code, high-performance scenario.
- ✗
DNN_CLASSIFIER
Why it's wrong here
DNN_CLASSIFIER is a deep neural network that can model complex patterns but requires more data and tuning to avoid overfitting. With only 30,000 examples, a boosted tree model is generally more robust and easier to train. Additionally, DNN_CLASSIFIER may need hyperparameter tuning and feature preprocessing, increasing complexity and operational overhead, which contradicts the low-code requirement.
- ✗
KMEANS
Why it's wrong here
KMEANS is an unsupervised clustering algorithm used for segmentation, not for binary classification like loan default prediction. It does not use labels and cannot predict a binary outcome. Using KMEANS would not address the supervised learning task, and the results would not provide default risk probabilities. Therefore, it is unsuitable for this scenario.
- ✗
LOGISTIC_REG
Why it's wrong here
LOGISTIC_REG is a linear model that may underperform on complex, non-linear relationships present in loan default data. With 30,000 examples and 25 features, a boosted tree model typically provides better accuracy. While logistic regression is simple and low-code, it is not the optimal choice when predictive performance is a priority and the dataset size supports more sophisticated models.
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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
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