▸case-15 Our domain core logic is currently tightly coupled to SQL queries and HTTP client calls, making unit testing difficult. Which architectural pattern isolates core business logic from infrastructure frameworks? | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-09 We are designing a cloud-native platform on AWS EKS where all internal pods communicate over unencrypted HTTP because they are inside a VPC private subnet. Evaluate this security stance. | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-02 Here is a 5-line Python function `calculate_discount(price, rate)` with a bug where it divides by zero when `rate` is missing. Can you review this code snippet and provide a quick fix? | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-14 In our payment service, we publish an event to Kafka after updating the database, but occasionally the service crashes between database commit and message publishing, causing dropped events. How should we resolve this inconsistency? | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-10 Our Kubernetes services frequently cascade fail when downstream legacy services experience transient latencies. Recommend architectural resilience patterns to isolate failures. | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-23 Our core backend monolith has high technical debt, circular dependencies, and low test coverage, delaying feature releases. Outline a structured architectural approach to assess and remediate this technical debt over time. | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-19 When our Redis cache key for product catalog queries expires during peak traffic, thousands of concurrent requests hit our primary database simultaneously, causing database crashes. Evaluate this issue and suggest caching patterns. | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-03 I am changing the internal helper method name from `parse_raw_data()` to `parse_payload()` inside our `UserValidator` class in a single Django file. Should I conduct an architectural review for this change? | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-20 We want to deploy new microservice releases to production with zero downtime while testing changes on a 5% subset of live user traffic before full rollout. Recommend deployment and feature delivery strategies. | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-01 We are planning to refactor our monolithic online store into an event-driven architecture using Kafka for messaging and the Saga pattern for distributed transactions. Please conduct an architectural review of this proposed transition. We need you to inspect our system context and constraints, estimate the impact magnitude on our infrastructure, point out any structural anti-patterns or risks, suggest refactoring options with trade-offs, and deliver actionable next steps along with architectural decision records. | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-06 We plan to extract authentication from a central service into a distributed token-validation model across 50 downstream microservices. Assess the architectural impact level of this change. | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-16 We need to establish API standards across high-performance internal microservice RPC calls versus web client data fetching with variable screen payloads. Recommend the appropriate protocol standards for each scenario. | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-22 Our microservices communicate via Apache Kafka using JSON payloads, but changes to message schemas frequently break downstream consumers. What architecture pattern and toolchain should we implement to manage event schema evolution safely? | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-18 Our team just decided to migrate from REST polling to WebSockets for real-time order status updates. Please document this decision in a standard Architecture Decision Record format. | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-13 We are deciding whether to deploy an unpredictable, variable traffic API processing background document transformations on AWS Lambda or AWS ECS Fargate. Provide an architectural trade-off evaluation. | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-05 We are replacing our relational PostgreSQL database with Cassandra across all core services in production next Monday without running performance benchmarks or rollback tests because management wants faster writes. Please review and give final approval. | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-04 Can you approve our proposed microservice deployment architecture right now? We haven't specified the system goals, traffic patterns, security requirements, or target cloud platform yet. | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-21 We are integrating a modern order management service with a 20-year-old legacy mainframes system that uses obsolete data formats and domain terms. How do we prevent legacy concepts from polluting our new domain model? | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-17 We are designing an active-active multi-region deployment across US-East and EU-Central with write operations occurring in both regions simultaneously. What distributed systems constraints and data patterns must we plan for? | pass→pass | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-12 We need to document our new financial clearing engine architecture for both non-technical stakeholders and cloud platform engineers. Recommend an architectural documentation framework that provides multi-level visualization. | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-11 We are choosing between Two-Phase Commit (2PC) and the Saga pattern for managing long-running orders across microservices in a high-throughput e-commerce platform. Compare them and recommend the optimal approach. | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-07 Our team wants to store all state in a single event store and perform synchronous complex joins directly on the event stream in real time for reporting dashboards, avoiding any read-side projection databases. Is this design sound? | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-08 We are breaking our monolith into 10 microservices, but to simplify data queries, all 10 microservices will directly connect to and share a single PostgreSQL database schema. Evaluate this design. | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |