PMLE Architecting Low-Code ML Solutions Practice Question
A data engineer wants to use BigQuery ML to train a model for predicting customer churn (binary classification) using a large dataset. They want the model to be automatically tuned. Which model type should they choose?
⚠ Common exam trap
It's easy for candidates to confuse 'automatically tuned' with models that have default hyperparameters (like LOGISTIC_REG or BOOSTED_TREE_CLASSIFIER), but only AUTOML_CLASSIFIER performs automated hyperparameter tuning and architecture search without requiring manual specification.
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
✓
AUTOML_CLASSIFIER
(AUTOML_CLASSIFIER) is correct because it automatically performs architecture search and hyperparameter tuning to find the best model for binary classification tasks, such as customer churn prediction. This is ideal when the data engineer wants the model to be automatically tuned without manual intervention, as AutoML handles feature engineering, model selection, and tuning under the hood.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
LOGISTIC_REG
Why it's wrong here
LOGISTIC_REG performs binary classification but does not itself automatically tune hyperparameters; BigQuery ML's automatic tuning applies to boosted tree and AutoML models. It is tempting because logistic regression is the standard, interpretable baseline for churn, and would be correct when manual hyperparameter control is acceptable.
- ✗
BOOSTED_TREE_CLASSIFIER
Why it's wrong here
BOOSTED_TREE_CLASSIFIER trains gradient-boosted trees, but BigQuery ML does not perform automatic hyperparameter tuning for it by default; tuning must be enabled explicitly. It is tempting because boosted trees excel on tabular churn data, yet the question specifies automatic tuning, which is a built-in feature of the correct option rather than this one.
- ✗
DNN_CLASSIFIER
Why it's wrong here
DNN_CLASSIFIER builds a feed-forward neural network whose hyperparameters (hidden layers, activation, dropout) require manual tuning via hyperparameter tuning options; it does not automatically tune itself. It is tempting because deep networks suit complex, high-dimensional churn patterns, but BOOSTED_TREE_CLASSIFIER with automatic hyperparameter tuning is the intended choice for tabular churn data.
- ✓
AUTOML_CLASSIFIER
Why this is correct
AUTOML_CLASSIFIER satisfies the automatic tuning constraint: BigQuery ML performs hyperparameter tuning and architecture search internally, so no manual configuration is needed. It handles binary classification directly, matching the churn prediction task, unlike boosted tree or logistic regression models, which require the engineer to specify tuning themselves.
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Same concept, more angles
1 more way this is tested on PMLE
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. 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?
medium- A.BOOSTED_TREE_CLASSIFIER
- B.LOGISTIC_REG
- ✓ C.DNN_CLASSIFIER
- D.DNN_REGRESSOR
Why C: 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.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PMLE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PMLE exam.