Practice Test Geeks(MAC) Marketing Analytics Certification Practice Test
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Data Collection & Management Flashcards

7 cards from real MAC practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.

Read the first 7 Data Collection & Management flashcards as text

  1. Which of the following is the correct order of a standard ETL pipeline?

    Answer: Extract → Transform → Load

    ETL stands for Extract, Transform, Load — data is first pulled from sources, cleaned/reshaped, then loaded into the destination system.

  2. What is 'data freshness' and why does it matter in marketing analytics?

    Answer: How recently data was collected or updated, affecting the relevance of insights for time-sensitive decisions

    Data freshness indicates how current the data is; stale data can lead to outdated insights that result in poor marketing decisions.

  3. Which privacy regulation primarily governs the collection and use of personal data for consumers in California?

    Answer: CCPA

    The California Consumer Privacy Act (CCPA) grants California residents rights over their personal data collected by businesses.

  4. A data pipeline fails silently and loads zero rows without throwing an error. What process would have caught this issue?

    Answer: Implementing data quality checks and row-count validation alerts

    Automated row-count and completeness checks alert teams when a pipeline loads unexpectedly low or zero records, catching silent failures.

  5. What is the key difference between 'structured' and 'unstructured' data in marketing analytics?

    Answer: Structured data is organized in predefined schemas (like tables), while unstructured data lacks a fixed format (like social media posts)

    Structured data fits neatly into rows and columns, whereas unstructured data (text, images, video) requires additional processing to extract insights.

  6. When a brand uses a data clean room to collaborate with a retail partner, what is the primary benefit?

    Answer: It allows both parties to analyze combined datasets without exposing raw personal data to each other

    Data clean rooms enable privacy-safe analysis of overlapping audiences by running queries on combined data without either party seeing the other's raw records.

  7. Which of the following scenarios represents a violation of data minimization principles?

    Answer: Collecting date of birth, income bracket, and home address for a simple email newsletter sign-up

    Data minimization requires collecting only the data necessary for the stated purpose; gathering unnecessary sensitive fields violates this principle.