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Data Acquisition and PreparationmediumMultiple SelectObjective-mapped

DA0-002 Data Acquisition and Preparation Practice Question

A data analyst is reviewing a dataset of customer transactions and wants to assess data quality by profiling the 'order_date' column. Which TWO profiling tasks are most appropriate for this date column? (Select 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

Count of null values

Profiling a date column typically includes checking for null values and verifying the data type. Cardinality and pattern analysis are more relevant for categorical or string columns; variance is for numeric data.

Answer analysis

Option-by-option breakdown

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

  • Pattern analysis (e.g., format consistency)

    Why it's wrong here

    Pattern analysis is typically for string columns, not date columns.

  • Count of null values

    Why this is correct

    Null count is a standard profiling check for any column.

  • Variance

    Why it's wrong here

    Variance is a statistical measure for numeric data, not applicable to dates.

  • Cardinality (number of unique values)

    Why it's wrong here

    Cardinality is more useful for categorical columns; for dates, range is often more informative.

  • Data type verification

    Why this is correct

    Verifying that the column is stored as a date type is an important quality check.

About these practice questions

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JA

Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

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.