The plain-text body thing tripped me up at first.
Start by clarifying the requirements and constraints, then outline the API endpoints and their HTTP methods, status codes, and response formats. Explain how you'll parse plain text request bodies and validate input, and describe how you'll handle edge cases like missing resources and bad input. Finally, discuss any trade-offs or design decisions, such as using a simple in-memory store versus a database.
Pro tip: Mention that you'll use appropriate HTTP status codes (e.g., 201 for creation, 400 for bad input, 404 for missing resources) and ensure responses are consistently JSON, even for errors. Also, highlight the importance of idempotency for PUT and DELETE operations.
Ask clarifying questions about the resource, expected request/response formats, authentication, persistence, and any specific edge cases. Confirm that request bodies are plain text and responses are JSON.
Define RESTful endpoints for CRUD operations: POST /notes (create), GET /notes/{id} (read), PUT /notes/{id} (update), DELETE /notes/{id} (delete). Specify the expected request body (plain text) and response body (JSON) for each.
Explain how you'll read the plain text body, validate it (e.g., non-empty, length limits), and handle malformed input. For example, return 400 Bad Request with a JSON error message if the body is invalid.
Describe the logic for each operation, including generating unique IDs, storing data (e.g., in-memory map), and handling missing resources (return 404). Ensure responses include appropriate status codes and JSON payloads.
Mention trade-offs like using an in-memory store for simplicity versus a database for persistence, and how you'd handle concurrency. Also, suggest possible extensions like pagination or authentication.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one is genuinely harder than it sounds.
Start by thoroughly analyzing the specification to identify inputs, outputs, and expected behavior. Then systematically design test cases covering normal cases, edge cases, and failure modes, using equivalence partitioning and boundary value analysis. Finally, structure your answer by walking through the test cases you would write, explaining your reasoning and how you would handle ambiguities in the spec.
Pro tip: Demonstrate maturity by acknowledging that specs are often incomplete or ambiguous; propose clarifying questions and suggest writing tests that document assumptions, which can later be validated with the implementer.
Read the spec carefully to identify the function's contract: inputs, outputs, preconditions, postconditions, and any constraints. Note any ambiguities or missing details that need clarification.
Brainstorm categories of test cases: normal/expected inputs, edge cases (boundaries, empty, null, extreme values), and failure modes (invalid inputs, exceptions, error handling).
For each scenario, define specific inputs and expected outputs. Use techniques like equivalence partitioning, boundary value analysis, and decision tables to ensure coverage.
Organize tests into logical groups (e.g., by functionality or input type). Prioritize based on risk and likelihood of failure, ensuring critical paths are covered first.
Clearly state any assumptions made due to spec gaps. Suggest how to handle them (e.g., ask for clarification, write tests that can be easily adjusted).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.