The correct action is to apply SMOTE, the Synthetic Minority Oversampling Technique, to oversample the readmission class. SMOTE directly addresses class imbalance by generating synthetic examples of the minority class rather than simply duplicating existing records, which forces the logistic regression model to learn meaningful decision boundaries instead of defaulting to the majority class. On the CompTIA Data+ DA0-001 exam, this scenario tests your understanding that accuracy is a misleading metric when classes are imbalanced—an 85% accuracy here is worthless because the model never predicts readmission. A common trap is choosing random undersampling of the majority class, but that discards valuable data, whereas SMOTE preserves information while balancing the dataset. Memory tip: SMOTE stands for “Synthetic Minority Oversampling Technique”—think of it as “smoothing” the minority class with new, realistic data points to help the model see the full picture.
DA0-001 Analyzing and Modeling Data Practice Question
This DA0-001 practice question tests your understanding of analyzing and modeling data. 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 healthcare analytics team is building a classification model to predict patient readmission within 30 days. The dataset contains 10,000 records with 30 features, including demographics, vital signs, lab results, and medication history. The target variable is imbalanced: 85% no readmission, 15% readmission. The team used logistic regression with default settings and achieved an accuracy of 85%, but the model predicted 'no readmission' for all patients. The lead analyst suspects the model is not learning due to class imbalance. The team has time to implement one corrective action before the next model review. Which action should the team take?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Apply SMOTE to oversample the readmission class
Option B is correct because SMOTE (Synthetic Minority Oversampling Technique) directly addresses the class imbalance by generating synthetic samples for the minority class (readmission). This forces the logistic regression model to learn decision boundaries that separate the two classes, rather than defaulting to the majority class prediction. With 85% majority and 15% minority, accuracy alone is misleading, and SMOTE is a proven technique to improve recall for the minority class.
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.
✗
Remove features with low variance to reduce noise
Why it's wrong here
Low variance features are not the primary issue; the model is not learning from the minority class.
✓
Apply SMOTE to oversample the readmission class
Why this is correct
SMOTE generates synthetic samples, balancing the classes and allowing the model to learn from the minority class.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use accuracy as the evaluation metric to monitor improvement
Why it's wrong here
Accuracy is misleading for imbalanced data; precision-recall or AUC should be used.
✗
Switch to a random forest model with default settings
Why it's wrong here
Without handling imbalance, random forest may still predict majority class.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose accuracy as a metric (Option C) because it seems intuitive, but in imbalanced datasets, accuracy is misleading and does not reflect model performance for the minority class.
Detailed technical explanation
How to think about this question
SMOTE works by selecting a minority class sample and finding its k-nearest neighbors (typically k=5), then generating synthetic samples along the line segments between the sample and its neighbors. This creates new, plausible data points in feature space, which helps the logistic regression model learn a more balanced decision boundary. In healthcare, this is critical because false negatives (missed readmissions) can lead to patient harm, and SMOTE improves recall without simply duplicating existing minority samples.
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 small business has 20 workstations on the 192.168.1.0/24 network and one public IP from its ISP. The router uses PAT (NAT overload) so all 20 devices share one public address using different source ports. NAT questions test whether you understand the four address terms and which direction each translation applies.
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.
Analyzing and Modeling Data — This question tests Analyzing and Modeling Data — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Apply SMOTE to oversample the readmission class — Option B is correct because SMOTE (Synthetic Minority Oversampling Technique) directly addresses the class imbalance by generating synthetic samples for the minority class (readmission). This forces the logistic regression model to learn decision boundaries that separate the two classes, rather than defaulting to the majority class prediction. With 85% majority and 15% minority, accuracy alone is misleading, and SMOTE is a proven technique to improve recall for the minority class.
What should I do if I get this DA0-001 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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A healthcare analytics team is analyzing patient readmission rates. They have a dataset with thousands of records including patient age, diagnosis, length of stay, number of prior admissions, and discharge date. The goal is to identify key factors influencing readmission and create a model to predict high-risk patients. The data is imbalanced: only 5% of patients are readmitted within 30 days. The team plans to use logistic regression. What is the most appropriate approach?
medium
A.Use the dataset as is because logistic regression handles imbalance
B.Remove most of the non-readmitted patients to balance the dataset
C.Use accuracy as the evaluation metric
✓ D.Apply oversampling techniques like SMOTE to the training set
Why D: With imbalanced data, logistic regression can be biased toward the majority class. Oversampling the minority class (e.g., SMOTE) helps the model learn patterns for readmission. Using accuracy as a metric would be misleading. Removing majority samples discards valuable data. Using data as-is often fails to predict the minority class.
Variation 2. A company is analyzing customer feedback sentiment. The dataset is highly imbalanced with 95% positive and 5% negative comments. Which technique should the analyst use to address class imbalance before modeling?
hard
A.Use accuracy as the evaluation metric
B.Undersample the majority class
C.Oversample the majority class
✓ D.Use SMOTE
Why D: SMOTE (Synthetic Minority Oversampling Technique) is the correct choice because it generates synthetic samples for the minority class (negative comments) by interpolating between existing minority instances, rather than simply duplicating them. This addresses the 95:5 imbalance without the information loss of undersampling or the overfitting risk of naive oversampling.
Last reviewed: Jun 11, 2026
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This DA0-001 practice question is part of Courseiva's free CompTIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DA0-001 exam.
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