Question 1

Describe a real-world use case for using Step Functions with Lambda in a data workflow.

Accepted Answer

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....

Question 2

Describe using Step Functions to handle retries and error notifications.

Accepted Answer

Architectural logic: Retry handles transient failures; Catch routes terminal failures to notifications or DLQ. Config: Retry with ErrorEquals, IntervalSeconds, BackoffRate, MaxAttempts; Catch with Next (e.g., NotifyFailure). Example: Lambda.ServiceException retry 3x with exponential backoff; States.ALL → NotifyFailure (SNS). Why: Observability; no lost failures....

Question 3

Explain how Access Control Lists (ACLs) can affect IAM role permissions.

Accepted Answer

Architectural logic: S3 has IAM and ACLs (legacy). Both must allow—ACLs can deny even if IAM allows. ACLs = per-object/bucket grants. IAM = identity-based. Trade-off: ACLs add complexity; prefer bucket policies + IAM....

Question 4

Explain how Step Functions integrate with other AWS services.

Accepted Answer

Architectural logic: Step Functions has native integrations—Lambda, Glue, ECS, SNS, SQS, DynamoDB, SageMaker, EventBridge. Each = Task state with service ARN and payload. Flow: Lambda validate → Glue ETL → Lambda write DynamoDB. Why: No polling; optimized integrations....

Question 5

Explain how using a staging area in S3 can help.

Accepted Answer

Architectural logic: Staging = buffer between producers and consumers. Benefits: Decouple ingestion from processing; absorb bursts; validate before load; replay without re-fetch. Flow: API → s3://staging/raw/ → Glue → s3://curated/ → Athena/Redshift. Why: Producers write async; consumers process batch. Cost: Staging storage; lifecycle to archive/delete....

Question 6

Explain the role of Glue Catalog in Athena.

Accepted Answer

Architectural role: Glue Catalog = metadata (schema, location, partitions); Athena = query engine. Athena reads S3 using Catalog metadata. Why: Single catalog for Glue, EMR, Athena—consistency. Best practice: Crawlers or manual tables; partition; columnar; one catalog.

Question 7

Explain using AWS Glue for ETL. What challenges might you face with large datasets?

Accepted Answer

Architectural use: Glue = serverless Spark; Catalog; connectors. Challenges at scale: (1) DPU sizing—OOM or slowness. (2) Job bookmarks—slow on many partitions. (3) Small files—poor performance; compact first. (4) Data skew—stragglers; salting. (5) Cost—DPU-hours....

Question 8

Explain using IAM roles for secure cross-account access to an S3 bucket.

Accepted Answer

Architectural flow: Account A (owner) bucket policy allows Account B role. Account B role has trust policy. Apps in B assume role → access A's bucket. Use external ID for extra security. Best practice: Least privilege; bucket policy; external ID; CloudTrail audit.

Question 9

How do you ensure message ordering in Kinesis Streams?

Accepted Answer

Architectural logic: Ordering is per partition key. Same key → same shard → ordered. Use consistent partition key (user_id, order_id) for causal ordering. Trade-off: Same key = potential hot partition. Best practice: Partition by entity ID; multiple keys for parallelism.

Question 10

How does the trust relationship policy in IAM roles work?

Accepted Answer

Architectural logic: Trust policy defines who can assume the role. Principal (account, user, role, service) + conditions (MFA, IP, tags). Example: Allow sts:AssumeRole for account 123456789012. Best practice: Least privilege; conditions; avoid * principal; document.

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