DA0-002 Data Analysis Practice Question
A data scientist is building a K-means clustering model for customer segmentation. After plotting the within-cluster sum of squares (WCSS) against the number of clusters (k), she observes that the WCSS decreases sharply until k=5 and then levels off. Which value of k should she choose based on the elbow method?
⚠ Common exam trap
DA0-002 often tests the misconception that the lowest WCSS (highest k) is best; candidates must recognize that the elbow method selects the inflection point, not the minimum WCSS.
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
✓
k=5
The elbow method plots WCSS against k and looks for the 'elbow' — the point where the rate of decrease sharply changes from steep to shallow. Since WCSS drops sharply until k=5 and then levels off, k=5 is the optimal choice because additional clusters beyond that yield diminishing returns.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
k=5
Why this is correct
The elbow method selects the k where WCSS reduction transitions from steep to shallow, forming the bend. At k=5 the curve flattens, meaning additional clusters yield diminishing compactness gains. Choosing k=5 satisfies the stem's observed inflection point, balancing model simplicity against within-cluster variance.
- ✗
k=6
Why it's wrong here
k=6 sits past the elbow, where additional clusters yield only marginal WCSS reduction, so the model overfits by splitting natural segments. The elbow method picks the bend at k=5. k=6 would be chosen only if the curve kept dropping sharply beyond five clusters.
- ✗
k=4
Why it's wrong here
k=4 lies before the elbow, so the WCSS is still falling steeply and clusters remain too coarse, merging customers who belong apart. The elbow method selects the bend at k=5. k=4 would be correct only if the curve had already flattened at four clusters.
- ✗
k=3
Why it's wrong here
The elbow method selects the k where the WCSS curve's rate of decrease flattens, which the stem places at k=5, not k=3. Choosing k=3 underfits the segmentation, merging distinct customer groups. k=3 would be defensible only if the curve had already levelled off at that point.
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.