DA0-002 Data Analysis Practice Question
An analyst uses K-means clustering on customer purchase data. After plotting the within-cluster sum of squares for different values of k, they observe an elbow at k=4. What is the most appropriate number of clusters?
⚠ Common exam trap
DA0-002 often tests the misconception that the elbow value itself is the answer versus confusing it with the 'optimal' k from silhouette analysis — candidates sometimes pick a value adjacent to the elbow or assume more clusters is always better.
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
✓
4
The elbow method plots within-cluster sum of squares (WCSS) against k and looks for the 'elbow' — the point where adding more clusters yields diminishing reductions in WCSS. An elbow at k=4 means the marginal gain from k=4 to k=5 is small, so k=4 is the recommended number of clusters. Choosing k=4 balances model simplicity with fit quality.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
4
Why this is correct
An elbow at k=4 indicates that adding further clusters yields diminishing reductions in within-cluster sum of squares, so four captures the data's structure without overfitting. Selecting four directly satisfies the stem's elbow-method criterion, giving the most appropriate cluster count for the purchase data.
- ✗
6
Why it's wrong here
Six clusters overshoot the elbow at k=4, splitting groups whose within-cluster sum of squares already plateaus. Over-clustering is tempting when pursuing tighter, purer segments, but the plot shows diminishing variance reduction beyond four, so the extra clusters fragment genuine structure.
- ✗
5
Why it's wrong here
The elbow at k=4 identifies four as the point of diminishing returns in within-cluster sum of squares; k=5 lies past it. Selecting one cluster beyond the elbow is tempting when maximising separation, yet each extra cluster adds complexity without comparable variance reduction.
- ✗
3
Why it's wrong here
The elbow marks where additional clusters stop yielding meaningful within-cluster sum of squares reduction, so k=4 is indicated; k=3 sits before that bend. Choosing one cluster fewer is tempting when seeking the most parsimonious model, but the plot's inflection point, not minimal k, determines the count.
Go deeper
Related to this question
About these practice questions
One of 1,004 original DA0-002 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
This DA0-002 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 DA0-002 exam.