DA0-002 Data Analysis Practice Question
A data scientist applies K-means clustering to a customer dataset. The elbow method suggests using 4 clusters. After running K-means with k=4, the within-cluster sum of squares (WCSS) is plotted against k, and the elbow is at k=4. What does this indicate?
⚠ Common exam trap
The trap is interpreting the elbow as a definitive indication of the true number of clusters; candidates must remember that the elbow method is a heuristic and does not guarantee that the data naturally forms that many clusters.
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
✓
Increasing k beyond 4 would not significantly reduce WCSS.
The elbow method plots the within-cluster sum of squares (WCSS) against the number of clusters k. The 'elbow' point indicates where the rate of decrease in WCSS sharply changes, meaning that adding more clusters beyond that point yields diminishing returns. Therefore, at k=4, increasing k further would not significantly reduce WCSS, suggesting 4 is a reasonable choice for the number of clusters.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Increasing k beyond 4 would not significantly reduce WCSS.
Why this is correct
The elbow marks where additional clusters stop yielding meaningful WCSS reduction. At k=4 the curve flattens, so moving to k=5 or beyond adds complexity without materially lowering within-cluster sum of squares, confirming four clusters as the sensible choice.
- ✗
The data naturally forms 4 clusters with no noise.
Why it's wrong here
The elbow suggests four clusters are a reasonable fit, but it cannot prove the data has no noise or exactly four natural groups. It is tempting because the elbow visually implies structure, yet WCSS reduction alone never confirms cluster purity or the absence of outliers.
- ✗
The algorithm converged to a local minimum.
Why it's wrong here
K-means always converges to a local minimum given fixed initialisation; the elbow at k=4 says nothing about convergence quality. It is tempting because local minima are a genuine K-means concern, but that issue is addressed by multiple restarts or k-means++, not by reading the WCSS elbow plot.
- ✗
The model has overfit the data.
Why it's wrong here
An elbow at k=4 indicates a sensible cluster count, not overfitting; overfitting would show as clusters capturing noise with poor generalisation. It is tempting because more clusters can fit training data tightly, but WCSS always decreases with k, so the elbow itself signals diminishing returns, not overfit.
About these practice questions
This DA0-002 question is part of Courseiva's 1,004-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. 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.