Start by clarifying requirements and scale, then present a layered architecture: data model, state machine, APIs, assignment, and analytics. Walk through the dual-review pipeline and conflict adjudication with concrete examples, and discuss how each layer scales and extends.
Pro tip: Emphasize idempotency and auditability in the state machine and APIs, as these are critical for reliable annotation workflows and analytics. Also, mention that you'd use event sourcing or an append-only log to capture all state changes for analytics and debugging.
Ask about scale (tasks per day, concurrent annotators), review types (dual-review, adjudication), revision policies, and analytics needs. Confirm non-functional requirements like latency, consistency, and extensibility.
Define core entities: Task, Annotation, Review, User, Assignment, and AuditLog. Use an append-only event log for state changes to support analytics and traceability.
Model task states (e.g., CREATED, ASSIGNED, ANNOTATING, REVIEWING, ADJUDICATING, REVISION, COMPLETED) and transitions. Ensure idempotent transitions and handle revision loops by allowing tasks to re-enter earlier states.
Expose RESTful or gRPC APIs for task management, annotation submission, review, and adjudication. Implement assignment strategies (e.g., round-robin, skill-based, load-balanced) and support dynamic re-assignment.
Use sharding, caching, and async processing for scale. Design for extensibility via pluggable review policies and annotation types. Build an analytics layer using materialized views or stream processing on the event log.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
They pulled this out as a specific follow-up after I described the submit-annotation endpoint.
Start by clarifying the requirements: what does idempotency mean for annotation submissions (e.g., preventing duplicate annotations from retries) and how concurrent annotators affect it. Then propose a design using idempotency keys and database constraints to ensure exactly-once semantics, and discuss trade-offs like performance and complexity.
Pro tip: Mention that idempotency keys should be generated client-side and stored with a unique constraint, and that you'd return the same response for duplicate requests to simplify client retry logic.
Ask questions to understand what idempotency means here: should duplicate submissions be ignored, merged, or rejected? How are retries triggered (client, network)? What is the expected concurrency level?
Propose using a client-generated idempotency key (e.g., UUID) sent with each submission. Store it in a database table with a unique constraint, along with the response, to detect and handle duplicates.
Use database transactions with appropriate isolation levels (e.g., serializable or unique constraint checks) to prevent race conditions. Consider optimistic concurrency control or distributed locks if needed.
Ensure that retries with the same idempotency key return the original response (or a 409 Conflict if still processing). Implement exponential backoff and jitter on the client side.
Compare database constraints vs. distributed locks vs. event sourcing. Address performance impact, storage overhead, and cleanup of old idempotency keys.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the current dual-review pipeline and the requirements for new shapes, then propose a flexible abstraction that decouples pipeline stages from their orchestration. Discuss how to model stages as composable units and use a configuration-driven approach to define different pipeline shapes, while addressing trade-offs in complexity, performance, and maintainability.
Pro tip: Emphasize that the goal is not to build a general workflow engine but to find the simplest abstraction that supports the known shapes and can evolve. Show awareness of over-engineering and suggest starting with a minimal viable abstraction, then iterating based on real needs.
Ask questions to understand the current dual-review pipeline, the specific new shapes (three-way review, single-pass), and non-functional requirements like latency, throughput, and consistency. Identify what varies and what stays the same across shapes.
Extract the core building blocks: stages (e.g., review, approval), transitions, and data flow. Propose modeling each stage as an independent, reusable component with well-defined inputs/outputs, and separate the pipeline definition from execution.
Introduce a pipeline definition (e.g., via configuration or DSL) that specifies the sequence and conditions of stages. Use a lightweight orchestrator that interprets the definition and manages state, retries, and error handling.
Discuss trade-offs: added complexity vs. flexibility, potential performance overhead, and impact on existing dual-review. Suggest how to migrate incrementally and how to ensure backward compatibility.
Walk through how the design supports three-way review and single-pass, including failure scenarios and dynamic changes. Mention testing strategies and monitoring to ensure correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining a common interface for assignment strategies, then explain how each strategy implements it differently. Describe how a factory or dependency injection allows swapping strategies at runtime or configuration time, and discuss trade-offs like fairness, efficiency, and complexity.
Pro tip: Emphasize that the strategy pattern decouples the assignment logic from the core system, making it easy to add new strategies without modifying existing code. Mention that you'd use feature flags or configuration to swap strategies in production without redeploying.
Create a common interface (e.g., AssignmentStrategy) with a method like assign(task, agents) that returns the selected agent. This ensures all strategies are interchangeable.
Maintain a circular counter or queue of agents. Each assignment picks the next agent in sequence, wrapping around. Simple but ignores agent skills and current load.
Filter agents by required skills for the task, then select among qualified agents (e.g., by least recent assignment or random). Requires a skill matrix or tagging system.
Track current load (e.g., number of active tasks) per agent and assign to the least loaded. May need real-time metrics and can be combined with skill filtering.
Use a factory or dependency injection to instantiate the desired strategy based on configuration. Allow runtime swapping via a strategy manager that holds the current strategy and can replace it.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I listed throughput, queue depth, time-to-completion, adjudication rate, and Cohen's kappa per annotator.
Start by clarifying the product context and what decisions the analytics layer should support, then propose a tiered metric set covering volume, quality, and throughput. For per-annotator agreement at scale, describe a streaming or batch pipeline that computes pairwise agreement incrementally, using sketches or sampling to bound cost while preserving statistical validity.
Pro tip: Emphasize that agreement metrics must be paired with calibration and adjudication workflows; raw agreement alone can incentivize gaming. Also mention that you'd version metric definitions and store raw annotation events so you can recompute agreement when the label schema evolves.
Ask what decisions the metrics will drive (e.g., quality control, annotator feedback, pricing) and what scale/latency requirements exist. This shapes whether you need real-time streaming or daily batch computation.
Propose metrics across three layers: volume (tasks completed, annotations per hour), quality (agreement, accuracy against gold, calibration), and operational (throughput, latency, cost per annotation). Tie each metric to a specific action.
Describe how to compute per-annotator agreement efficiently: use incremental statistics (e.g., Cohen's kappa, Krippendorff's alpha) over pairwise comparisons, pre-aggregate by item and annotator, and leverage sampling or approximate sketches for high-volume streams.
Explain how to handle millions of annotations: partition by project/time, use map-reduce or streaming windows, cache intermediate results, and choose between exact vs. approximate methods based on cost and required precision.
Mention how you'd monitor metric drift, detect annotator degradation, and feed insights back into training and task design. Highlight the importance of versioning and reproducibility.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.