The correct approach is to normalize each rating to a 0-100 scale using min-max normalization. This method applies the formula (x - min) / (max - min) * 100, which rescales every original value proportionally within its own channel’s range—whether 1-10, 1-5, or 1-7—while preserving the relative distribution of responses. For the CompTIA Data+ DA0-001 exam, this question tests your understanding of data standardization techniques, specifically how to handle survey data with different scales without distorting the underlying patterns. A common trap is choosing simple multiplication or binary conversion, which would compress or inflate certain ranges unevenly. Remember that min-max normalization is ideal when you need a single comparable metric across channels because it accounts for each scale’s full range. Memory tip: think “min-max maps to max consistency”—the formula forces every scale into the same 0-100 window while keeping each response’s rank intact.
DA0-001 Comparing and Contrasting Data Concepts Practice Question
This DA0-001 practice question tests your understanding of comparing and contrasting data concepts. 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 telecommunications company is experiencing issues with its customer satisfaction survey data. The data is collected from multiple channels: phone, email, and web forms. Each channel uses a different scale for ratings: phone uses 1-10, email uses 1-5, and web uses 1-7. Additionally, some survey responses contain missing values for demographic fields. The data analyst needs to calculate an overall satisfaction score that is comparable across all channels. The company's leadership wants a single metric that minimizes distortion from the different scales. Which approach should the analyst use to standardize the ratings?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Normalize each rating to a 0-100 scale using min-max normalization.
Option A is correct because min-max normalization rescales each rating to a common 0-100 range using the formula (x - min) / (max - min) * 100. This preserves the relative distribution of responses within each channel while eliminating the effect of different scale lengths, making the scores directly comparable. It minimizes distortion better than simple multiplication or binary conversion, as it accounts for the full range of each original scale.
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 each rating to a 0-100 scale using min-max normalization.
Why this is correct
Correct: Min-max normalization maps each scale to a common range, preserving relative differences.
Related concept
Read the scenario before looking for a memorised answer.
✗
Calculate the average rating separately for each channel and then compare the averages.
Why it's wrong here
Incorrect: Averages are not directly comparable due to scale differences.
✗
Convert all ratings to a binary metric of satisfied (above midpoint) or unsatisfied.
Why it's wrong here
Incorrect: Binning loses information about degree of satisfaction.
✗
Convert all ratings to a 1-10 scale by multiplying email ratings by 2 and web by 1.43.
Why it's wrong here
Incorrect: This linear conversion assumes proportional intervals, which may not be valid.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may think simple multiplication (Option D) is sufficient for scale conversion, but Cisco tests the understanding that linear scaling without considering the full range and distribution can introduce distortion, whereas min-max normalization is the proper technique for creating a comparable metric across different scales.
Detailed technical explanation
How to think about this question
Min-max normalization is a linear transformation that maps the original range to a target range, preserving the shape of the distribution. In practice, if the data contains outliers, min-max normalization can compress the majority of values into a narrow band; a robust alternative would be z-score standardization, but the question asks for a method that minimizes distortion from different scales, and min-max is appropriate here. Real-world survey data often uses different scales, and normalization is a standard preprocessing step before aggregation in business intelligence tools like Tableau or Power BI.
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 small business has 20 workstations on the 192.168.1.0/24 network and one public IP from its ISP. The router uses PAT (NAT overload) so all 20 devices share one public address using different source ports. NAT questions test whether you understand the four address terms and which direction each translation applies.
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.
Comparing and Contrasting Data Concepts — This question tests Comparing and Contrasting Data Concepts — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Normalize each rating to a 0-100 scale using min-max normalization. — Option A is correct because min-max normalization rescales each rating to a common 0-100 range using the formula (x - min) / (max - min) * 100. This preserves the relative distribution of responses within each channel while eliminating the effect of different scale lengths, making the scores directly comparable. It minimizes distortion better than simple multiplication or binary conversion, as it accounts for the full range of each original scale.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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