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.'
This easy-level Cloud/Tools question appears frequently in data engineering interviews at companies like Capco. While less common, it tests deeper understanding that distinguishes strong candidates.
Start by clearly defining the core concept being asked about. Interviewers want to see that you understand the fundamentals before diving into implementation details. Structure your answer with a definition, then explain the practical application with a concise example.
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.
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