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 machine learning engineer is deploying a model that predicts customer churn. The model outputs probabilities between 0 and 1. The business requires that at least 90% of customers flagged for churn actually churn (precision >= 0.9). Currently, the model's precision is 0.85 at the default threshold of 0.5. Which threshold adjustment should the engineer consider?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "least"
Why it matters: You want the option with minimum overhead, fewest steps, or lowest impact — not the most feature-rich or comprehensive answer.
Decrease the threshold to 0.4
Why wrong: Lower threshold typically decreases precision.
Decrease the threshold to 0.3
Why wrong: Decreasing threshold increases sensitivity but reduces precision.
Increase the threshold to 0.7
Higher threshold increases precision by requiring higher confidence for positive predictions.
Keep the threshold at 0.5
Why wrong: Current precision is below 0.9, so no change will not meet requirement.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Increase the threshold to 0.7
Increasing the threshold to 0.7 raises the probability cutoff for classifying a customer as churning. This means only customers with a high predicted probability (strong model confidence) are flagged, which reduces false positives and increases precision. Since the current precision at 0.5 is 0.85 and the goal is ≥0.9, moving the threshold higher is the correct direction to achieve the required precision.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Decrease the threshold to 0.4
Why it's wrong here
Lower threshold typically decreases precision.
Decrease the threshold to 0.3
Why it's wrong here
Decreasing threshold increases sensitivity but reduces precision.
Increase the threshold to 0.7
Why this is correct
Higher threshold increases precision by requiring higher confidence for positive predictions.
Clue confirmation
The clue word "least" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Keep the threshold at 0.5
Why it's wrong here
Current precision is below 0.9, so no change will not meet requirement.
Common exam traps
The trap here is that candidates often associate higher thresholds with lower recall and assume precision will drop, but in reality, increasing the threshold filters out low-confidence positives, which reduces false positives and increases precision.
Detailed technical explanation
Precision is defined as TP/(TP+FP). By raising the decision threshold, the model becomes more conservative, reducing the number of positive predictions (TP+FP). If the model's probability estimates are well-calibrated, higher thresholds yield higher precision because the remaining positive predictions are those with the strongest evidence. In practice, the engineer should evaluate the precision-recall curve to find the exact threshold that achieves precision ≥0.9, as the relationship between threshold and precision is monotonic but not necessarily linear.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Increase the threshold to 0.7 — Increasing the threshold to 0.7 raises the probability cutoff for classifying a customer as churning. This means only customers with a high predicted probability (strong model confidence) are flagged, which reduces false positives and increases precision. Since the current precision at 0.5 is 0.85 and the goal is ≥0.9, moving the threshold higher is the correct direction to achieve the required precision.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "least". You want the option with minimum overhead, fewest steps, or lowest impact — not the most feature-rich or comprehensive answer.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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