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Data Store ManagementeasyMultiple ChoiceObjective-mapped

DEA-C01 Data Store Management Practice Question

An e-commerce application uses Amazon ElastiCache for Redis to cache product catalog data. The cache currently uses lazy loading. The team wants to ensure that frequently accessed product data is always fresh. Which caching strategy should they implement?

⚠ Common exam trap

Candidates often assume lazy loading with a short TTL is sufficient for freshness, but the exam tests the understanding that only write-through (or write-behind) strategies guarantee synchronous cache updates without relying on expiration windows.

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

Write-through caching

Write-through caching ensures that data is written to the cache simultaneously with the database, guaranteeing that frequently accessed product data is always fresh. This strategy eliminates stale reads by synchronously updating the cache on every write, which directly addresses the requirement for freshness without relying on expiration or lazy population.

Answer analysis

Option-by-option breakdown

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

  • Write-through caching

    Why this is correct

    Write-through updates cache directly on writes, ensuring data is always fresh.

  • Set a TTL of 5 minutes for all cached items

    Why it's wrong here

    TTL does not guarantee freshness if data changes within the TTL window.

  • Use database read replicas to serve data

    Why it's wrong here

    Read replicas are for database scaling, not caching.

  • Lazy loading with TTL

    Why it's wrong here

    Lazy loading may serve stale data until TTL expires.

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Last reviewed: Jun 11, 2026

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