The answer is to use the ML.PREDICT function with a lower classification threshold, such as 0.3 instead of the default 0.5. This low-code approach directly improves recall for fraud detection because lowering the threshold causes the model to classify more transactions as positive (fraud), capturing more true positives at the cost of some false positives. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding of the precision-recall trade-off in imbalanced datasets, where high accuracy can be misleading due to class skew. A common trap is to assume retraining or resampling is required, but the exam emphasizes that threshold tuning via ML.PREDICT is a fast, code-efficient adjustment. Remember: when recall is low, lower the bar—adjusting the threshold in the prediction function, not the model, is the quick fix.
PMLE Architecting low-code ML solutions Practice Question
This PMLE practice question tests your understanding of architecting low-code ml solutions. Examine the command output carefully: the correct answer depends on what the output actually shows, not on general recall alone. 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.
Network Topology
Refer to the exhibit. A data scientist trained a BigQuery ML classification model to detect fraudulent transactions. The dataset has 95% non-fraud (class 0) and 5% fraud (class 1). The evaluation metrics show high accuracy (0.91) but low recall (0.60) for fraud detection. Which low-code approach should the data scientist take to improve recall without significantly sacrificing precision?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use the ML.PREDICT function with a lower classification threshold (e.g., 0.3 instead of 0.5) to capture more positive cases.
Option A is correct because lowering the classification threshold in ML.PREDICT (e.g., from 0.5 to 0.3) causes the model to classify more transactions as fraud, directly increasing recall. This is a low-code adjustment that does not require retraining or complex feature engineering, and it allows the data scientist to trade off precision for recall as needed.
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.
✓
Use the ML.PREDICT function with a lower classification threshold (e.g., 0.3 instead of 0.5) to capture more positive cases.
Why this is correct
Lowering the threshold increases recall by classifying more instances as positive.
Related concept
Read the scenario before looking for a memorised answer.
✗
Apply feature selection to reduce the number of features and focus on the most predictive ones.
Why it's wrong here
May not improve recall and could reduce model performance.
✗
Increase the number of training iterations by setting the MAX_ITERATIONS option to a higher value.
Why it's wrong here
Does not address the recall issue directly.
✗
Re-train the model using AutoML Tables with class weights to penalize false negatives more heavily.
Why it's wrong here
AutoML Tables is not BigQuery ML; the model is already in BigQuery ML.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that improving recall always requires retraining or complex model changes, when in fact a simple threshold adjustment in ML.PREDICT is a valid low-code technique to shift the precision-recall balance.
Detailed technical explanation
How to think about this question
In BigQuery ML, the default classification threshold is 0.5, meaning predictions with a probability ≥0.5 are labeled as class 1. By lowering the threshold, more borderline cases are flagged as fraud, which increases recall (true positives / (true positives + false negatives)) but may also increase false positives, reducing precision. This trade-off is visualized in the precision-recall curve, and the optimal threshold can be selected using the ML.EVALUATE function's metrics or by plotting the curve.
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: Use the ML.PREDICT function with a lower classification threshold (e.g., 0.3 instead of 0.5) to capture more positive cases. — Option A is correct because lowering the classification threshold in ML.PREDICT (e.g., from 0.5 to 0.3) causes the model to classify more transactions as fraud, directly increasing recall. This is a low-code adjustment that does not require retraining or complex feature engineering, and it allows the data scientist to trade off precision for recall as needed.
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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