Describe a basic lifecycle policy to manage cloud storage costs

WHAT THIS TESTS: The interviewer wants to see if you understand cloud cost optimization through automated object lifecycle management rather than manual intervention. They are checking whether you know the difference between hot warm and cold storage tiers and whether you can map business requirements to concrete retention and transition rules. At the senior level they also care if you consider edge cases like existing objects transition costs and retrieval penalties.

A GOOD ANSWER COVERS: A strong response starts by defining a clear timeline with specific day thresholds. For example recent model artifacts and training datasets stay in S3 Standard for the first 30 days to support active experimentation. After 30 days you transition them to S3 Standard-IA for infrequent access because they are no longer needed daily but might be retrieved for retraining. After 90 days you move them to S3 Glacier Flexible Retrieval for long-term archive at roughly one fifth the cost. Finally you set an expiration action to delete objects after one year or whatever retention mandate applies. You should mention that S3 Lifecycle rules apply retroactively to existing objects and automatically to new ones so you do not need to backfill. You should also flag that there are per request ingestion charges for each transition so moving data too aggressively can backfire and that retrieval costs climb as you move to colder tiers.

COMMON WRONG ANSWERS: A red flag is proposing a custom cron job or script to delete files instead of using the cloud provider native lifecycle engine. Another mistake is ignoring the minimum storage duration requirement for example deleting from Standard-IA or Glacier before the minimum period incurs a prorated charge. Some candidates also forget that lifecycle transitions are not instantaneous though billing stops at the eligibility time except for transitions into Intelligent-Tiering. Proposing to compress or deduplicate data without first tiering it is also weaker because it skips the easiest cost win.

LIKELY FOLLOW-UPS: The interviewer might ask how you would handle versioning since old versions of model artifacts can accumulate quickly and should have their own expiration rule. They might ask what you would do if a data scientist needs emergency access to a Glacier archived artifact which should trigger a discussion about retrieval tiers and latency. They could also ask how you monitor whether the policy is actually saving money pointing to S3 Storage Lens or cost and usage reports. Finally they might probe whether you would use Intelligent-Tiering instead of explicit day based rules which is a valid alternative but has different billing semantics.

ONE CONCRETE EXAMPLE: Suppose your team stores 100 terabytes of training snapshots and model checkpoints. Without a lifecycle policy everything sits in S3 Standard at roughly 23 dollars per terabyte per month costing about 2300 dollars monthly. By implementing a rule that transitions data to Standard-IA after 30 days and Glacier Flexible Retrieval after 90 days you could drop the blended average cost to under 10 dollars per terabyte per month cutting the bill by more than half while keeping data available for compliance. The key is aligning the 30 day and 90 day thresholds with your actual retraining and audit patterns rather than picking round numbers arbitrarily.

Source: docs.aws.amazon.com