I started with the data model which felt like the safe entry point.
Start by clarifying requirements and constraints, then propose a modular architecture with a rule repository, condition evaluator, and action executor. Walk through the data model for rules and expenses, and discuss evaluation strategies (e.g., priority, conflict resolution) and trade-offs like performance vs. flexibility.
Pro tip: Emphasize extensibility and maintainability: design rules as composable predicates and actions, and consider using a domain-specific language (DSL) or configuration-driven approach to allow non-developers to define rules. Also, discuss how to handle rule conflicts and versioning.
Ask about rule complexity, expected volume, latency requirements, and who defines rules (developers vs. business users). Clarify decision outcomes (approve, reject, escalate) and whether rules can be combined with AND/OR logic.
Design schemas for Expense (amount, category, submitter, date) and Rule (id, priority, condition, action). Consider representing conditions as expression trees or predicate functions for flexibility.
Outline components: Rule Repository (stores rules), Rule Evaluator (matches expenses against conditions), Conflict Resolver (handles multiple matches), and Action Executor (performs decisions). Discuss evaluation order (priority, specificity).
Explain how to evaluate rules efficiently (e.g., indexing, caching) and resolve conflicts (e.g., first-match, priority-based, or combining actions). Mention strategies like decision tables or Rete algorithm for complex scenarios.
Compare approaches: hardcoded vs. configurable rules, performance vs. flexibility, and simplicity vs. feature richness. Propose how to support rule versioning, testing, and dynamic updates.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements and constraints of the expense rule system, then propose a deterministic conflict resolution strategy that balances business needs with technical feasibility. Discuss trade-offs between different approaches (e.g., priority-based, specificity-based, or user-defined) and emphasize the importance of auditability and configurability.
Pro tip: Mention that conflict resolution should be configurable per organization or policy, and that you'd log all matches and the chosen rule for audit purposes—this shows you understand enterprise needs and compliance.
Ask about the types of rules, frequency of conflicts, and business priorities (e.g., compliance vs. cost savings). Understand if rules are user-defined or system-defined.
Propose strategies like priority-based (explicit priority field), specificity-based (most specific rule wins), or first-match (order-based). Discuss pros and cons of each.
Ensure the chosen strategy is deterministic and logged. Include metadata in the resolution (e.g., which rule matched and why) for debugging and compliance.
Evaluate how the conflict resolution impacts performance as the number of rules grows. Suggest indexing or caching if needed.
Recommend making the conflict resolution policy configurable per organization or user, and design for future rule types or strategies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements and existing predicate system, then propose a composite pattern where predicates are nodes in a tree. Explain how to implement AND, OR, and NOT as composite predicates that recursively evaluate child predicates, and discuss evaluation strategies like short-circuiting and potential optimizations.
Pro tip: Mention that you would use the Composite design pattern and emphasize short-circuit evaluation to improve performance, as it shows you think about both design and efficiency. Also, consider how to handle null or missing fields gracefully.
Ask questions to understand the existing predicate system, expected usage, and constraints such as performance, extensibility, and whether predicates are combined dynamically or statically.
Propose a tree-based structure where leaf predicates represent simple conditions and composite predicates (AND, OR, NOT) contain child predicates. Define a common interface with an evaluate method.
Describe how each composite predicate evaluates its children: AND returns true only if all children are true, OR returns true if any child is true, and NOT negates its single child's result.
Discuss short-circuit evaluation for AND and OR to avoid unnecessary computations, and consider caching or indexing for repeated evaluations.
Address null handling, empty child lists, and how to add new operators (e.g., XOR) without modifying existing code, following the Open/Closed principle.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with a DB-backed rules store with a short TTL cache in the service layer.
Start by clarifying the types of rules and the desired update frequency, then propose a design that externalizes rules from code—such as a rules engine with a database or configuration store—and includes a safe deployment pipeline with versioning and rollback. Emphasize how the system will fetch, validate, and apply rules at runtime without service restarts, and discuss trade-offs like performance and consistency.
Pro tip: Highlight the importance of a staging environment and canary releases for rule changes, and mention that you would include audit logs and monitoring to quickly detect and revert problematic rules—this shows you think about production safety, not just the happy path.
Ask about the nature of the rules (e.g., business logic, validation, routing), who will author them, how often they change, and the acceptable latency for rule updates.
Propose storing rules in an external system like a database, configuration service (e.g., etcd, Consul), or a dedicated rules engine (e.g., Drools, JSON-based DSL), ensuring they are versioned and auditable.
Explain how the service will fetch rules dynamically—e.g., via polling, watch mechanisms, or a sidecar—and how it will compile/cache them for performance, with fallback to last-known-good rules.
Describe a pipeline for validating and testing new rules (e.g., unit tests, staging), then rolling them out gradually (canary) with monitoring and automatic rollback on errors.
Discuss how to handle concurrent rule updates, ensure eventual consistency across instances, and minimize overhead (e.g., in-memory caching, incremental updates).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.