The correct approach is to train a model using the original data, then adjust the decision threshold on the validation set to maximize recall while keeping precision above 90%. This works because classification models output probability scores, and the default threshold of 0.5 is rarely optimal for imbalanced datasets like fraud detection (99% legitimate, 1% fraudulent). By lowering the threshold, you capture more true positives (increasing recall), but you must validate on a holdout set to ensure precision stays above 90%, as too low a threshold will flood the system with false positives. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this tests your understanding that threshold tuning is a post-training hyperparameter, not a data or model architecture fix—a common trap is choosing SMOTE or weighted loss, which alter training but offer no direct precision guarantee. Remember the mnemonic: "Train first, then tune the gate—recall up, precision wait."
MLS-C01 Practice Question: Machine Learning Implementation and Operations
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. 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 team is building a fraud detection system using Amazon SageMaker. The training data is highly imbalanced (99% legitimate, 1% fraudulent). They need to maximize the recall of the fraud class while keeping precision above 90%. Which approach should they take?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Train a model using the original data, then adjust the decision threshold on the validation set to maximize recall while precision > 90%
Option D is correct because adjusting the model threshold after training to favor recall while monitoring precision is the most direct way to meet the business requirement. Option A (SMOTE) can help but may not guarantee precision. Option B (weighted loss) is good but less direct than threshold tuning. Option C (random undersampling) may discard too much data.
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.
✗
Undersample the majority class to create a balanced dataset and train a Random Forest
Why it's wrong here
Undersampling loses data and may not achieve required precision.
✓
Train a model using the original data, then adjust the decision threshold on the validation set to maximize recall while precision > 90%
Why this is correct
Threshold tuning directly optimizes recall with a precision constraint.
Related concept
Read the scenario before looking for a memorised answer.
✗
Train an XGBoost model with scale_pos_weight parameter set to 99
Why it's wrong here
Weighted training helps but does not directly control precision; threshold tuning is still needed.
✗
Use SMOTE to oversample the fraud class and then train a logistic regression
Why it's wrong here
SMOTE can improve recall but may reduce precision; threshold tuning is needed.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
Got this wrong? Here's your next step.
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Train a model using the original data, then adjust the decision threshold on the validation set to maximize recall while precision > 90% — Option D is correct because adjusting the model threshold after training to favor recall while monitoring precision is the most direct way to meet the business requirement. Option A (SMOTE) can help but may not guarantee precision. Option B (weighted loss) is good but less direct than threshold tuning. Option C (random undersampling) may discard too much data.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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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