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AI Associate Data for AI Practice Question

A company is preparing customer data to train a custom AI model for sentiment analysis. Which two data preparation best practices should they follow? (Choose two.)

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

Ensure data is representative of all customer demographics.

Ensuring representative data and anonymizing PII are critical for model fairness and privacy. Removing all records with missing values can discard useful information; using only recent data may introduce bias; single-annotator labeling can cause subjective bias.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Use only data from the last month.

    Why it's wrong here

    Using only recent data may miss long-term patterns and reduce sample size, harming model robustness.

  • Ensure data is representative of all customer demographics.

    Why this is correct

    Representative data prevents model bias and improves generalization across customer segments.

  • Remove all records with missing values.

    Why it's wrong here

    Removing all missing values may discard valuable data; imputation or handling missingness is preferred.

  • Label data manually by a single annotator.

    Why it's wrong here

    Single-annotator labeling introduces individual bias; best practice is multiple annotators with consensus.

  • Anonymize personally identifiable information (PII) before training.

    Why this is correct

    Anonymization protects customer privacy and complies with regulations like GDPR.

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