- A
Use SMOTE to generate synthetic positive samples
SMOTE creates synthetic minority samples to balance classes.
- B
Use evaluation metrics like precision-recall AUC instead of accuracy
Precision-recall AUC is more informative for imbalanced data.
- C
Apply L2 regularization to the model
Why wrong: Regularization prevents overfitting but does not address class imbalance.
- D
Use AUC-ROC as the sole evaluation metric
Why wrong: AUC-ROC can be misleading in extreme imbalance; it's a metric, not a technique.
- E
Assign higher class weights to the minority class
Class weights penalize misclassification of minority class more.
Question 56 of 1,000
AIF-C01 AI and ML Fundamentals Practice Question
This AIF-C01 practice question tests your understanding of ai and ml fundamentals. 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 financial services company is building an ML model to detect money laundering. The dataset is highly imbalanced (0.1% positive cases). Which THREE techniques can help address the class imbalance? (Select THREE.)
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use SMOTE to generate synthetic positive samples
SMOTE (Synthetic Minority Oversampling Technique) generates synthetic samples for the minority class by interpolating between existing minority class instances and their nearest neighbors. This increases the representation of the positive class in the training data, directly addressing the severe imbalance (0.1% positive cases) without simply duplicating existing samples, which can lead to overfitting.
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 SMOTE to generate synthetic positive samples
Why this is correct
SMOTE creates synthetic minority samples to balance classes.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Use evaluation metrics like precision-recall AUC instead of accuracy
Why this is correct
Precision-recall AUC is more informative for imbalanced data.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Apply L2 regularization to the model
Why it's wrong here
Regularization prevents overfitting but does not address class imbalance.
- ✗
Use AUC-ROC as the sole evaluation metric
Why it's wrong here
AUC-ROC can be misleading in extreme imbalance; it's a metric, not a technique.
- ✓
Assign higher class weights to the minority class
Why this is correct
Class weights penalize misclassification of minority class more.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the distinction between techniques that address class imbalance directly (like SMOTE and class weighting) versus general regularization or evaluation metrics that do not solve the core problem of skewed class distribution.
Detailed technical explanation
How to think about this question
SMOTE works by selecting a minority class sample, finding its k-nearest neighbors (typically k=5), and creating a synthetic sample along the line segment connecting the sample to a randomly chosen neighbor. This introduces diversity compared to naive oversampling. In practice, SMOTE can be combined with undersampling of the majority class (e.g., SMOTEENN) to further clean noisy overlaps. The choice of k and the feature space distance metric (e.g., Euclidean) can significantly impact synthetic sample quality, especially in high-dimensional financial transaction data.
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.
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FAQ
Questions learners often ask
What does this AIF-C01 question test?
AI and ML Fundamentals — This question tests AI and ML Fundamentals — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use SMOTE to generate synthetic positive samples — SMOTE (Synthetic Minority Oversampling Technique) generates synthetic samples for the minority class by interpolating between existing minority class instances and their nearest neighbors. This increases the representation of the positive class in the training data, directly addressing the severe imbalance (0.1% positive cases) without simply duplicating existing samples, which can lead to overfitting.
What should I do if I get this AIF-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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Last reviewed: Jul 4, 2026
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