The answer is that the churned class is underrepresented, causing the model to favor the majority class. This is the most likely cause because AutoML Tables optimizes for overall accuracy by default, and when a dataset has severe class imbalance, the model achieves high accuracy simply by predicting the majority class for nearly every instance. Low recall for the churned class is the classic symptom of this bias—the model rarely identifies the minority class correctly. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding of how automated ML handles imbalanced data and the pitfalls of relying solely on accuracy as a metric. A common trap is to assume high accuracy means a good model, but you must always check recall and precision for the minority class. Remember the memory tip: “High accuracy, low recall? Check the class ball.”
PMLE Architecting low-code ML solutions Practice Question
This PMLE practice question tests your understanding of architecting low-code ml solutions. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A retail company wants to build a customer churn prediction model using AutoML Tables. They have a dataset with 5000 rows and 50 features, including customer ID, transaction history, and support tickets. The target is a binary column 'churned'. After training, the model shows high accuracy but low recall for the churned class. What is the most likely cause?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The churned class is underrepresented, causing the model to favor the majority class.
Option C is correct because in imbalanced datasets, AutoML Tables optimizes for overall accuracy, which can be high if the majority class dominates. Low recall for the churned class indicates the model predicts most instances as non-churned, a classic symptom of class imbalance. AutoML Tables provides class weighting and sampling options to mitigate this, but without them, the model favors the majority class.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
The dataset is too small for AutoML to train effectively.
Why it's wrong here
5000 rows is sufficient for AutoML; the issue is class imbalance.
✗
The features are not normalized, leading to biased predictions.
Why it's wrong here
AutoML handles scaling internally.
✓
The churned class is underrepresented, causing the model to favor the majority class.
Why this is correct
Class imbalance leads to high accuracy but low recall for minority class.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
The dataset includes a unique customer ID feature, causing overfitting.
Why it's wrong here
Customer ID is not predictive; AutoML ignores high-cardinality features.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that high accuracy always means a good model, trapping candidates who overlook class imbalance as the root cause of poor recall for the minority class.
Detailed technical explanation
How to think about this question
Under the hood, AutoML Tables uses techniques like neural architecture search and ensemble methods, but its default loss function (e.g., log loss) treats all classes equally unless configured with class weights. In practice, a real-world churn dataset often has <10% churned customers, and without adjusting the decision threshold or using resampling, the model's probability estimates are skewed toward the majority class, leading to low recall. AutoML Tables offers a 'class imbalance' flag and a 'weighted' option to address this, but they are not enabled by default.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Architecting low-code ML solutions — This question tests Architecting low-code ML solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The churned class is underrepresented, causing the model to favor the majority class. — Option C is correct because in imbalanced datasets, AutoML Tables optimizes for overall accuracy, which can be high if the majority class dominates. Low recall for the churned class indicates the model predicts most instances as non-churned, a classic symptom of class imbalance. AutoML Tables provides class weighting and sampling options to mitigate this, but without them, the model favors the majority class.
What should I do if I get this PMLE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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