This MLA-C01 practice question tests your understanding of ml model development. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 data scientist is training a binary classification model using Amazon SageMaker. The dataset has a severe class imbalance (95% negative, 5% positive). The model achieves 99% accuracy but fails to identify positive cases correctly. Which action should the data scientist take to improve the model's ability to detect positive cases?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use the F1 score as the evaluation metric and adjust the classification threshold based on the precision-recall curve.
Option D is correct because in a severely imbalanced dataset (95% negative, 5% positive), accuracy is misleading. The F1 score balances precision and recall, and adjusting the classification threshold based on the precision-recall curve allows the model to prioritize recall for the minority class, directly improving detection of positive cases. This approach is recommended in SageMaker when using built-in algorithms or custom models with imbalanced 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.
✗
Switch to a logistic regression model with balanced class weights.
Why it's wrong here
Model choice alone does not guarantee improved recall.
✗
Use accuracy as the evaluation metric and retrain the model.
Why it's wrong here
Accuracy is misleading for imbalanced classes.
✗
Apply SMOTE (Synthetic Minority Over-sampling Technique) to the training data.
Why it's wrong here
SMOTE is a valid approach but not the most direct action for improving detection of positive cases.
✓
Use the F1 score as the evaluation metric and adjust the classification threshold based on the precision-recall curve.
Why this is correct
F1 score and threshold tuning directly address the imbalance.
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 think oversampling (SMOTE) or changing the model type is the primary fix, but the exam tests understanding that evaluation metrics and threshold tuning are critical for imbalanced classification, not just data preprocessing.
Detailed technical explanation
How to think about this question
The precision-recall curve plots precision (TP/(TP+FP)) against recall (TP/(TP+FN)) for different thresholds, and the F1 score is the harmonic mean of precision and recall. In SageMaker, you can use the built-in XGBoost or Linear Learner with objective='binary:logistic' and then manually adjust the threshold (e.g., from 0.5 to 0.3) after training to increase recall for the minority class. A real-world scenario is fraud detection, where missing a positive case (fraud) is far more costly than a false alarm.
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.
ML Model Development — This question tests ML Model Development — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use the F1 score as the evaluation metric and adjust the classification threshold based on the precision-recall curve. — Option D is correct because in a severely imbalanced dataset (95% negative, 5% positive), accuracy is misleading. The F1 score balances precision and recall, and adjusting the classification threshold based on the precision-recall curve allows the model to prioritize recall for the minority class, directly improving detection of positive cases. This approach is recommended in SageMaker when using built-in algorithms or custom models with imbalanced data.
What should I do if I get this MLA-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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