The answer is to add engineered features like rolling transaction count and velocity per user. This is the most effective approach because logistic regression models rely heavily on feature engineering to capture complex patterns, and high false positives in fraud detection often stem from a lack of temporal context. By incorporating rolling aggregates and velocity metrics computed from real-time streaming data, the model gains discriminative signals about user behavior over time, allowing it to distinguish legitimate spikes in activity from actual fraud without sacrificing recall. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding that feature engineering is a primary lever for improving model precision, especially with simpler models like logistic regression, and that real-time feature computation can be leveraged without changing the model architecture. A common trap is to assume you need a more complex model or threshold tuning, but the core issue here is missing behavioral signals. Memory tip: think "velocity beats complexity" — when false positives plague a linear model, enrich the features, not the algorithm.
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 financial services company uses BigQuery ML to build a logistic regression model for fraud detection. The model is trained on the last 6 months of transaction data (about 50 million rows). After deployment, the fraud detection team notices a high false positive rate, causing customer dissatisfaction and extra manual review costs. The model is currently retrained monthly. The team wants to reduce false positives without sacrificing recall. They have access to real-time transaction streaming and can compute new features quickly. What is the most effective approach?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Add engineered features like rolling transaction count and velocity per user
Option D is correct because adding engineered features like rolling transaction count and velocity per user directly addresses the high false positive rate by providing the logistic regression model with more discriminative temporal signals. Since the team has access to real-time streaming and can compute features quickly, these features capture behavioral patterns that reduce false positives without sacrificing recall, and logistic regression can effectively leverage them with proper feature engineering.
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.
✗
Replace logistic regression with gradient boosted trees (XGBoost) in BigQuery ML
Why it's wrong here
Boosted trees might improve but without new features, false positives may persist.
✗
Use Vertex AI AutoML Tables to train a more complex model
Why it's wrong here
AutoML may not reduce false positives if the root cause is missing features.
✗
Increase retraining frequency to daily
Why it's wrong here
More frequent retraining on same features won't reduce false positives.
✓
Add engineered features like rolling transaction count and velocity per user
Why this is correct
New features provide more signal to reduce false positives.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume a more complex model (XGBoost or AutoML) is always better for reducing false positives, but the question specifically tests the principle that feature engineering—especially temporal aggregations—is the most effective lever when the model is already appropriate and data is streaming.
Detailed technical explanation
How to think about this question
Rolling transaction count and velocity features are time-windowed aggregations (e.g., COUNT or SUM over the last hour) that capture behavioral velocity, which is a strong indicator of fraud in streaming data. In BigQuery ML, these can be computed using SQL window functions with PARTITION BY user_id and ORDER BY transaction_timestamp, then materialized as features for the logistic regression model. Real-world fraud detection systems often rely on such features because they adapt to user behavior changes quickly, reducing false positives from legitimate high-frequency transactions.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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: Add engineered features like rolling transaction count and velocity per user — Option D is correct because adding engineered features like rolling transaction count and velocity per user directly addresses the high false positive rate by providing the logistic regression model with more discriminative temporal signals. Since the team has access to real-time streaming and can compute features quickly, these features capture behavioral patterns that reduce false positives without sacrificing recall, and logistic regression can effectively leverage them with proper feature engineering.
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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