PMLE Architecting Low-Code ML Solutions Practice Question
A healthcare company wants to build a model to predict patient readmission risk using structured data in BigQuery. They have a dataset with 100,000 rows and 30 features, including numerical and categorical variables. They require a model that provides explainable predictions and can be trained quickly. They decide to use BigQuery ML. Which model type should they choose?
⚠ Common exam trap
The trap here is assuming that a more complex model like a deep neural network is always better, ignoring the need for explainability.
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
✓
Logistic regression
Logistic regression is the best choice because it is interpretable, fast to train, and effective for binary classification with structured data. It provides coefficients that indicate feature importance, which helps explain predictions. BigQuery ML supports logistic regression with options for regularization, and it can handle categorical variables automatically. This aligns with the requirement for explainable predictions and quick training.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Matrix factorization
Why it's wrong here
Matrix factorization is typically used for recommendation systems, such as collaborative filtering, where the goal is to predict user-item interactions. It is not designed for binary classification of tabular data like patient readmission. The healthcare company needs a model that can predict a binary label based on patient features, so matrix factorization is not appropriate here.
- ✓
Logistic regression
Why this is correct
Logistic regression is a linear model for binary classification that provides interpretable coefficients, making it suitable for explainable predictions. It trains quickly on structured data and handles both numerical and categorical features after preprocessing. For predicting patient readmission (a binary outcome), logistic regression is a strong choice in BigQuery ML, especially when explainability is required. It also supports regularization to prevent overfitting.
- ✗
K-means clustering
Why it's wrong here
K-means is an unsupervised learning algorithm used for clustering, not for predicting a binary outcome like patient readmission. It does not use labels and cannot provide predictions for individual patients. Therefore, it is not suitable for this supervised classification task. The company needs a model that predicts risk, which requires a supervised approach such as logistic regression.
- ✗
Deep neural network (DNN)
Why it's wrong here
A deep neural network can model complex relationships but is less interpretable than logistic regression. The healthcare company requires explainable predictions, which DNNs do not easily provide. Additionally, DNNs may require more training time and hyperparameter tuning. While BigQuery ML supports DNNs, they are overkill for this structured dataset and do not meet the explainability requirement as directly as logistic regression.
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