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Data Concepts and EnvironmentsmediumMultiple ChoiceObjective-mapped

DA0-002 Data Concepts and Environments Practice Question

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?

⚠ Common exam trap

Watch out — candidates often think simple multiplication (Option D) is sufficient for scale conversion, but the understanding that linear scaling without considering the full range and distribution can introduce distortion is key, whereas min-max normalization is the proper technique for creating a comparable metric across different scales.

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 each rating to a 0-100 scale using min-max normalization.

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.

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.

  • 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.

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