Which approach is recommended for managing ECS data lifecycle in a cost-effective manner?

Question

Options

• AStoring all data indefinitely, regardless of its value
• BImplementing automated data tiering and archival policies
• CManual migration of data to cheaper storage classes as needed
• DUsing premium storage for all data types to ensure performance

• A
  8% (3)
• B
  85% (33)
• C
  5% (2)
• D
  3% (1)

Explanation

Implementing automated data tiering and archival policies (B) is the recommended approach because it systematically moves data through lifecycle phases (e.g., hot → warm → cold → frozen/delete in Elasticsearch ILM, or S3 lifecycle rules) based on age and access patterns - reducing storage costs without requiring constant manual intervention.

Why the distractors are wrong:

• A is wrong because storing all data indefinitely ignores cost efficiency; old, rarely accessed data should be archived or deleted, not kept on expensive hot storage forever.
• C is wrong because manual migration is error-prone, labor-intensive, and doesn't scale - humans forget, act inconsistently, and introduce delays that waste money.
• D is wrong because premium storage is unnecessary and expensive for infrequently accessed or archival data; performance tiers should match access frequency.

Memory tip: Think of it as a "data conveyor belt" - automation moves data from expensive, fast storage to cheap, slow storage as it ages, then discards it when it's no longer needed. Automate the belt, don't push by hand, and never let it run forever.

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