Reviewed by Aditya Kumar · Last reviewed 2026-03-24
Use case: ML inference and reporting pipeline. Raw events land in S3; Lambda validates; Step Functions orchestrates: preprocessing Lambda → external ML API (wait) → result Lambda writes to DynamoDB/S3 → Slack summary. Why Step Functions + Lambda: Lambda = stateless, short...
Red Flag: Using Step Functions for heavy compute (use Glue/EMR). Pro-Move: 'We use Map state to fan-out 10K events/day; Catch routes failures to DLQ; Standard Workflow for human approval step—execution graph gives full traceability.'
Use case: ML inference and reporting pipeline. Raw events land in S3; Lambda validates; Step Functions orchestrates: preprocessing Lambda → external ML API (wait) → result Lambda writes to DynamoDB/S3 → Slack summary. Why Step Functions + Lambda: Lambda = stateless, short compute; Step Functions = state, retries, branching, observability. Architectural trade-off: Express Workflows for high-volume, short runs (cheaper); Standard for long-running, complex branching. Cost: Step Functions charges per state transition; Lambda per invoke. For 10K events/day with Map state: ~100K transitions—cost is low. Pro-move: Parallel Map with Catch blocks for failure routing to DLQ; always define retry/backoff.
Red Flag: Using Step Functions for heavy compute (use Glue/EMR). Pro-Move: 'We use Map state to fan-out 10K events/day; Catch routes failures to DLQ; Standard Workflow for human approval step—execution graph gives full traceability.'
According to DataEngPrep.tech, this is one of the most frequently asked Cloud/Tools interview questions, reported at 1 company. DataEngPrep.tech maintains an editor-reviewed database of 1,863 data engineering interview questions across 7 categories.