DA0-002 Data Analysis Practice Question
A data scientist is using K-means clustering with k=3. After the first iteration, the centroids are recalculated. Which step occurs next in the algorithm?
⚠ Common exam trap
The trap is thinking the algorithm stops after centroid recalculation — candidates who confuse 'k is fixed' with 'algorithm is done' pick option B, but K-means iterates assignment and update until convergence.
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
✓
Assign each point to the nearest centroid
After centroids are recalculated, K-means returns to the assignment step, reassigning each data point to the nearest (typically Euclidean distance) centroid. This assignment-then-update loop repeats until convergence — either centroids stop moving, assignments stabilize, or a maximum iteration count is reached. So the next step after recalculation is reassignment.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Calculate the sum of squared errors
Why it's wrong here
Sum of squared errors is computed to assess convergence or compare clusterings, not as the mandatory next step after centroid recalculation. The algorithm must first reassign each point to its nearest updated centroid. SSE evaluation belongs to convergence checking or post-hoc cluster evaluation.
- ✗
Stop the algorithm because k is fixed
Why it's wrong here
K-means iterates until convergence or a set iteration limit, so stopping merely because k is fixed halts before reassignment and centroid updates stabilise. Fixed k is set at initialisation, not a termination trigger. Stopping applies only once assignments stop changing or the iteration cap is reached.
- ✗
Compute the elbow curve
Why it's wrong here
The elbow curve is a separate model-selection technique, plotting within-cluster SSE against varying k values to choose k before training. It is not part of the K-means iteration sequence. After recalculating centroids, the algorithm reassigns points to the nearest centroid.
- ✓
Assign each point to the nearest centroid
Why this is correct
Reassigning every point to its nearest centroid follows each recalculation, since K-means alternates between these two phases until convergence. After the first iteration's centroid update, the algorithm must reassign points before recalculating again, satisfying the stem's "which step occurs next" constraint.
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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.