Start by clarifying the requirements: which language, how the API key is stored, and whether retries or token refresh are needed. Then outline a clean design that centralizes authentication in a reusable HTTP client, and discuss trade-offs like security, testability, and error handling.
Pro tip: Mention that you would never hardcode the API key—use environment variables or a secrets manager—and that you'd add idempotency keys for safe retries, which is especially important for payment APIs like Stripe.
Ask about the language, API auth scheme (e.g., Bearer token, API key), and whether the key rotates or expires. Confirm if retries, timeouts, and logging are expected.
Propose a wrapper around an HTTP library that injects the Authorization header on every request. Keep the client configurable for base URL, timeouts, and retry policy.
Load secrets from environment variables or a secrets manager, never from source code. If tokens expire, implement a refresh mechanism with thread-safe caching.
Handle 401/403 by refreshing or failing fast, and retry transient 5xx/429 errors with exponential backoff and jitter. Log request IDs and avoid logging secrets.
Write unit tests with a mocked HTTP layer and integration tests against a sandbox. Discuss trade-offs like centralization vs. flexibility, and sync vs. async clients.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I went with base64 inside a JSON envelope, which worked, but I second-guessed myself mid-explanation.
Start by clarifying the API's expected content type and encoding, then describe how you would structure the payload (e.g., multipart/form-data or base64-encoded JSON). Emphasize handling large files efficiently and ensuring data integrity through checksums or validation.
Pro tip: Mention idempotency keys to prevent duplicate invoice uploads, a common concern in payment APIs like Stripe's. Also, discuss trade-offs between base64 encoding (simpler but larger payload) and multipart/form-data (more efficient for binary files).
Ask or determine the expected content type (e.g., multipart/form-data, application/json) and encoding (e.g., base64, raw binary) from the API documentation.
Decide between base64 encoding (for JSON payloads) or multipart/form-data (for direct binary upload), considering file size and API constraints.
For multipart, include the file as a part with appropriate headers (Content-Type, filename). For JSON, embed the base64 string in a field along with metadata.
Discuss streaming or chunked uploads to avoid memory issues and timeouts, especially for large invoices.
Include checksums (e.g., MD5, SHA-256) to verify file integrity and use HTTPS to secure transmission.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The reconciliation logic itself wasn't hard.
Start by clarifying the data sources, schemas, and reconciliation rules (e.g., matching keys, tolerance for amounts, timezone handling). Then outline a robust algorithm that parses the API response, joins it with the local data, and systematically compares amount, date, and status fields to flag discrepancies. Finally, discuss error handling, performance, and how you would present or log the discrepancies.
Pro tip: Mention idempotency and retry logic for API calls, and consider using a checksum or hash of the record to quickly detect changes. Also, highlight the importance of logging discrepancies with enough context for debugging.
Ask about the API response format (JSON, XML), local data source (database, CSV), and the matching key (e.g., transaction ID). Confirm reconciliation rules: exact match vs. tolerance for amounts, date format/timezone, and status mappings.
Parse the API response into a structured format (e.g., list of objects). Normalize fields: convert amounts to a common currency/unit, parse dates to a standard timezone, and map statuses to a canonical set.
Join the parsed API data with the local data using the matching key. For each matched pair, compare amount, date, and status. For unmatched records, flag as missing in one source.
For each field, determine if the difference is significant (e.g., amount beyond tolerance, date beyond a threshold, status mismatch). Categorize discrepancies by type and severity.
Address edge cases: missing fields, null values, duplicate keys, and API errors. Decide on output format: a report, log entries, or alerts. Discuss performance for large datasets (e.g., streaming, indexing).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Classic cursor vs offset question in disguise.
Start by clarifying the pagination style (cursor-based vs. offset-based) and the consistency guarantees, then outline a robust iteration strategy that handles edge cases like concurrent updates and rate limits. Emphasize idempotent processing and reconciliation checks to ensure all records are captured exactly once.
Pro tip: Mention that cursor-based pagination is generally preferred for large, changing datasets because it avoids duplicates and missed records from offset shifts, and highlight the importance of using a stable sort key and checkpointing progress for resumability.
Ask whether the API uses cursor-based or offset-based pagination, and what consistency guarantees exist (e.g., snapshot isolation, eventual consistency). This determines the iteration strategy and edge-case handling.
Iterate through pages using the provided cursor or offset, persisting the last-seen cursor/offset to enable resuming after failures. Use a stable sort key (e.g., created_at, id) to avoid duplicates or missed records.
Implement exponential backoff with jitter for retries on 429/5xx errors, and respect Retry-After headers. Ensure idempotent processing so retries don't create duplicate side effects.
After fetching all pages, compare the count or checksum against a known total (if available) or use a secondary query to detect missing records. Log any discrepancies for investigation.
If records can change during pagination, use a snapshot or versioning to ensure consistency. For offset-based pagination, consider fetching by a stable time window and deduplicating by ID.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by explaining how you handle transient failures with retries, exponential backoff, and jitter, then discuss idempotency keys to ensure safe retries. Emphasize that reconciliation must be idempotent by design, using unique identifiers and upserts to avoid duplicate side effects.
Pro tip: Mention that you always include a unique idempotency key in the request header (like Stripe's Idempotency-Key) and store it server-side to deduplicate retries, and that you use a reconciliation process that is naturally idempotent by comparing state and applying only necessary changes.
Explain what constitutes a transient failure (e.g., network timeouts, 5xx errors, rate limits) and how to distinguish them from permanent failures.
Describe using exponential backoff with jitter, setting a maximum retry limit, and only retrying on transient errors.
Explain how to generate and attach a unique idempotency key to each request so that retries are safe and don't cause duplicate operations.
Describe how reconciliation jobs should be idempotent: use unique identifiers, compare current state, and apply changes only if needed (e.g., upserts).
Mention the importance of monitoring retry rates and reconciliation outcomes, and testing idempotency with chaos engineering or fault injection.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about contract testing and mocking the HTTP layer for unit tests, then using a sandbox environment for integration tests.
Start by clarifying the reconciliation logic and the constraints of testing against a live or staging API, then outline a layered testing strategy that balances realism with safety. Emphasize using staging for most tests, with controlled live testing using test mode or sandbox environments, and highlight the importance of idempotency, data isolation, and observability.
Pro tip: Always design your tests to be idempotent and reversible, and use Stripe's test clocks or test mode to simulate time-based scenarios without affecting real data. This shows you understand the criticality of not disrupting production systems.
Identify what the reconciliation does, its inputs/outputs, and what aspects need testing (e.g., correctness, performance, error handling). Determine the acceptable level of risk for testing against live vs. staging.
Use staging for most tests, and leverage Stripe's test mode or sandbox for live-like testing. Utilize test clocks to simulate time progression and webhook events to trigger reconciliation.
Create test data that is isolated (e.g., unique identifiers) and ensure operations are idempotent to avoid side effects. Include edge cases like partial failures, duplicate events, and out-of-order events.
Add logging, metrics, and tracing to monitor reconciliation runs. Validate results by comparing expected vs. actual outcomes, and set up alerts for discrepancies.
Run tests in a controlled manner, monitor for issues, and clean up test data to avoid pollution. For live testing, use feature flags or canary releases to limit impact.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.