The answer is to decrease the probability threshold for the positive class. Lowering the threshold means the model will flag an equipment failure at a lower predicted probability, capturing more true positives and thereby increasing recall. This works because recall measures the proportion of actual failures correctly identified; by making the classifier more sensitive, you reduce false negatives at the cost of allowing more false positives, which is acceptable when missing a failure carries a high business impact. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your understanding of post-training calibration and the precision-recall trade-off, often appearing in scenarios with imbalanced datasets. A common trap is to assume you must retrain the model or adjust class weights, but threshold tuning is a simpler, direct lever. Memory tip: “Lower the bar to catch more stars” — decreasing the threshold lets in more positives, boosting recall.
MLS-C01 Modeling Practice Question
This MLS-C01 practice question tests your understanding of modeling. 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 company uses an XGBoost model to predict equipment failures. The model has high precision but low recall. The business impact of a false negative is very high (missing a failure). Which action would MOST effectively increase recall while keeping precision reasonably high?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Decrease the probability threshold for the positive class
Decreasing the probability threshold for the positive class means the model will classify a case as a failure at a lower predicted probability, which captures more true positives (increases recall). However, this also allows more false positives, so precision may drop, but the trade-off is acceptable given the high cost of false negatives. This is a standard post-training calibration technique for imbalanced classification problems.
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.
✗
Increase the regularization parameter lambda
Why it's wrong here
Regularization reduces overfitting but may not increase recall.
✗
Set the objective to 'reg:squarederror'
Why it's wrong here
Changing to regression objective is inappropriate.
✓
Decrease the probability threshold for the positive class
Why this is correct
Lower threshold increases recall but may reduce precision.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the number of boosting rounds
Why it's wrong here
More rounds may overfit but not guarantee recall improvement.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that increasing boosting rounds or regularization directly improves recall, when in fact the probability threshold is the primary lever for trading off precision and recall after training.
Detailed technical explanation
How to think about this question
In XGBoost, the default probability threshold is 0.5, but for imbalanced datasets where the positive class (failures) is rare, the optimal threshold is often much lower (e.g., 0.2 or 0.3). This is determined by plotting a precision-recall curve or ROC curve and selecting the threshold that maximizes recall while maintaining acceptable precision. In production, this threshold can be tuned using a validation set that reflects the real-world cost of false negatives versus false positives.
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.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Decrease the probability threshold for the positive class — Decreasing the probability threshold for the positive class means the model will classify a case as a failure at a lower predicted probability, which captures more true positives (increases recall). However, this also allows more false positives, so precision may drop, but the trade-off is acceptable given the high cost of false negatives. This is a standard post-training calibration technique for imbalanced classification problems.
What should I do if I get this MLS-C01 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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