Integration Patterns and API Design Questions

Focuses on integration concepts, data flow, and API design as the foundation for connecting systems and services. Coverage includes data integration techniques such as ETL and ELT, change data capture, data warehousing, synchronization and eventual consistency challenges, latency and throughput considerations, middleware and messaging solutions, and common integration patterns used in marketing and enterprise stacks. For APIs, topics include what APIs are and why they matter for developer products, REST versus GraphQL trade offs and use cases, HTTP methods and semantics, authentication and authorization patterns, rate limiting and throttling, versioning strategies, idempotency and error handling, documentation and developer experience, monitoring and service level considerations, and how API choices affect product and business decisions.

EasyTechnical

0 practiced

Define ETL and ELT and describe the technical differences between them. As a Solutions Architect working with marketing and analytics teams, explain scenarios where ETL is still preferable to ELT and scenarios where ELT (transform in the warehouse) is a better choice, including considerations around compute, governance, and schema complexity.

EasyTechnical

0 practiced

As a Solutions Architect, explain in plain language what an API is and why APIs are foundational for developer products. Provide two concrete business use cases (for example: partner integrations that drive revenue or internal APIs that accelerate product development), and describe one measurable metric you would report to product leadership to show API value.

HardTechnical

0 practiced

Implement (in Python or pseudocode) a function verify_webhook(headers, body, key_store) to validate webhook HMAC-SHA256 signatures while supporting key rotation and replay protection. Requirements: accept multiple active keys for verification, verify a timestamp within a TTL, store/validate nonces to prevent replay, and use constant-time comparison for signatures. Describe key-store semantics and how to rotate keys without dropping valid requests.

EasyTechnical

0 practiced

Define eventual consistency and contrast it with strong consistency. Provide a concrete integration example (such as user profile replication across services) where eventual consistency is acceptable and one example where it is not acceptable, explaining the user or business impact in each case.

HardSystem Design

0 practiced

Design an automated SDK generation and distribution pipeline that consumes an OpenAPI spec and produces idiomatic client libraries for Java, Python, and JavaScript. Address generated vs hand-written code separation, pagination and retry strategies, authentication plugins, packaging and publishing, versioning, and a process for handling SDK-specific behaviors not representable in the API spec.

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