- A
Normalize the features and set a fixed random seed for the initial centroids.
Normalization ensures all features contribute equally, and a fixed seed ensures reproducible results.
- B
Switch to hierarchical clustering, which does not require specifying k.
Why wrong: Hierarchical clustering also has parameters; the immediate issue is scaling and initialization, not the clustering algorithm itself.
- C
Increase the number of clusters to k=10 to capture more detail.
Why wrong: Increasing k may not address instability; it might even increase variability.
- D
Use principal component analysis (PCA) to reduce the number of features to two.
Why wrong: PCA can help with dimensionality but does not address the instability caused by random initialization and unscaled data.
Quick Answer
The answer is to normalize the features and set a fixed random seed for the initial centroids. This is correct because k-means clustering is highly sensitive to feature scale—variables like total spent can dominate distance calculations if not normalized—and its random centroid initialization causes different cluster assignments on each run. On the CompTIA Data+ DA0-001 exam, this question tests your understanding of data preprocessing and reproducibility in unsupervised learning; a common trap is to only normalize or only set a seed, but both steps are needed for consistent k-means clustering results. Remember the mnemonic “Scale and Seed” to lock in the two-part fix: scale the data so each feature contributes equally, then seed the random generator so the algorithm starts from the same point every time.
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 marketing analyst wants to segment customers based on their purchase history, including total spent, number of transactions, and average order value. The analyst runs k-means clustering with k=5 on the raw data but notices that the cluster assignments change significantly every time the algorithm is executed. What should the analyst do first to obtain consistent and meaningful clusters?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"first"Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
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
Normalize the features and set a fixed random seed for the initial centroids.
The instability in cluster assignments is caused by the algorithm's sensitivity to the scale of features and the random initialization of centroids. Normalizing the features ensures that each variable contributes equally to the distance calculations, while setting a fixed random seed makes the initial centroid selection deterministic, leading to reproducible results.
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.
- ✓
Normalize the features and set a fixed random seed for the initial centroids.
Why this is correct
Normalization ensures all features contribute equally, and a fixed seed ensures reproducible results.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Switch to hierarchical clustering, which does not require specifying k.
Why it's wrong here
Hierarchical clustering also has parameters; the immediate issue is scaling and initialization, not the clustering algorithm itself.
- ✗
Increase the number of clusters to k=10 to capture more detail.
Why it's wrong here
Increasing k may not address instability; it might even increase variability.
- ✗
Use principal component analysis (PCA) to reduce the number of features to two.
Why it's wrong here
PCA can help with dimensionality but does not address the instability caused by random initialization and unscaled data.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may think the instability is due to the choice of k or the algorithm itself, rather than recognizing that k-means is sensitive to feature scaling and random initialization, which are the first things to address for consistency.
Detailed technical explanation
How to think about this question
K-means clustering uses Euclidean distance, which is heavily influenced by the magnitude of features; without normalization, features like 'total spent' (e.g., thousands) dominate 'number of transactions' (e.g., single digits). Setting a random seed (e.g., via `random_state` in scikit-learn) forces the algorithm to use the same initial centroids each run, eliminating variability from the random initialization step.
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 practitioner preparing for the DA0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
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.
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FAQ
Questions learners often ask
What does this DA0-001 question test?
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: Normalize the features and set a fixed random seed for the initial centroids. — The instability in cluster assignments is caused by the algorithm's sensitivity to the scale of features and the random initialization of centroids. Normalizing the features ensures that each variable contributes equally to the distance calculations, while setting a fixed random seed makes the initial centroid selection deterministic, leading to reproducible results.
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.
Are there clue words in this question I should notice?
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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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