The answer is to increase the decision threshold for classifying a positive and to apply an anomaly detection algorithm like Isolation Forest. Raising the threshold forces the model to require higher confidence before flagging a failure, which directly reduces false positives and improves precision without drastically cutting into recall, provided the model’s probability scores are well-calibrated. Meanwhile, anomaly detection methods like Isolation Forest are designed to isolate rare events by partitioning the feature space, making them naturally effective for improving precision in highly imbalanced settings where the minority class is only 1%. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding of threshold tuning and algorithm selection for imbalanced classification, a common trap being to blindly add more class weights or resample, which can harm recall. A useful memory tip is “Threshold up, false positives down; Isolation Forest for rare crowns.”
PMLE Solving business challenges with ML Practice Question
This PMLE practice question tests your understanding of solving business challenges with ml. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 manufacturing company wants to predict equipment failure using sensor data. The data is highly imbalanced (only 1% failures). They are using a gradient boosted tree model with class weights. The model achieves 0.99 recall but 0.2 precision on the test set. Which two actions should they take to improve precision without significantly hurting recall? (Choose TWO)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Try an anomaly detection algorithm like Isolation Forest
Option B is correct because anomaly detection algorithms like Isolation Forest are designed to identify rare events by isolating anomalies rather than modeling the majority class, which can improve precision when the minority class is extremely rare (1%). Option E is correct because increasing the decision threshold for classifying a positive reduces false positives by requiring higher confidence for a positive prediction, directly improving precision while only minimally reducing recall if the model's probability scores are well-calibrated.
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.
✗
Oversample the minority class using SMOTE
Why it's wrong here
Oversampling may lead to overfitting and does not guarantee precision improvement.
✓
Try an anomaly detection algorithm like Isolation Forest
Why this is correct
Anomaly detection is designed for imbalanced data and can improve precision by focusing on outliers.
Related concept
Read the scenario before looking for a memorised answer.
✗
Add more features to the model
Why it's wrong here
More features may help but do not directly address the precision-recall trade-off.
✗
Increase the class weight for the minority class
Why it's wrong here
This would make the model even more sensitive, likely reducing precision further.
✓
Increase the decision threshold for classifying a positive
Why this is correct
A higher threshold reduces false positives, improving precision, while still catching most failures if threshold is tuned.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that oversampling or adding features always improves model performance, but in highly imbalanced scenarios, these actions can degrade precision without recall benefit, and the correct approach is to adjust the decision threshold or use anomaly detection.
Detailed technical explanation
How to think about this question
Gradient boosted trees with class weights adjust the loss function to penalize misclassifying the minority class more, but this can lead to overconfident predictions on borderline cases. Increasing the decision threshold shifts the operating point on the precision-recall curve, trading off recall for precision; this is often done by analyzing the probability distribution of predictions using a validation set. Isolation Forest works by randomly partitioning the feature space and measuring the path length to isolate a point—shorter paths indicate anomalies—making it effective for high-dimensional sensor data where failures are rare and distinct.
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.
Solving business challenges with ML — This question tests Solving business challenges with ML — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Try an anomaly detection algorithm like Isolation Forest — Option B is correct because anomaly detection algorithms like Isolation Forest are designed to identify rare events by isolating anomalies rather than modeling the majority class, which can improve precision when the minority class is extremely rare (1%). Option E is correct because increasing the decision threshold for classifying a positive reduces false positives by requiring higher confidence for a positive prediction, directly improving precision while only minimally reducing recall if the model's probability scores are well-calibrated.
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.
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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