Testing, Quality & Reliability Topics
Quality assurance, testing methodologies, test automation, and reliability engineering. Includes QA frameworks, accessibility testing, quality metrics, and incident response from a reliability/engineering perspective. Covers testing strategies, risk-based testing, test case development, UAT, and quality transformations. Excludes operational incident management at scale (see 'Enterprise Operations & Incident Management').
Observability Fundamentals and Alerting
Core principles and practical techniques for observability including the three pillars of metrics logs and traces and how they complement each other for debugging and monitoring. Topics include instrumentation best practices structured logging and log aggregation, trace propagation and correlation identifiers, trace sampling and sampling strategies, metric types and cardinality tradeoffs, telemetry pipelines for collection storage and querying, time series databases and retention strategies, designing meaningful alerts and tuning alert signals to avoid alert fatigue, dashboard and visualization design for different audiences, integration of alerts with runbooks and escalation procedures, and common tools and standards such as OpenTelemetry and Jaeger. Interviewers assess the ability to choose what to instrument, design actionable alerting and escalation policies, define service level indicators and service level objectives, and use observability data for root cause analysis and reliability improvement.
Your QA Background and Experience Summary
Craft a clear, concise summary (2-3 minutes) of your QA experience covering: types of applications you've tested (web, mobile, etc.), testing methodologies you've used (manual, some automation), key tools you're familiar with (test management tools, bug tracking systems), and one notable achievement (e.g., 'I identified a critical data loss bug during regression testing that prevented a production outage').
Logging Tracing and Debugging
Covers design and implementation of observability and diagnostic tooling used to troubleshoot applications and distributed systems. Topics include structured machine readable logging, log enrichment with context and correlation identifiers, log aggregation and indexing, retention and cost trade offs, and searchable queryability. It also includes distributed tracing to follow request flows across services, trace sampling and propagation, and correlating traces with logs and metrics. For debugging, include production safe debugging techniques, live inspection tools, core dump and profiling strategies, and developer workflows for reproducing and isolating issues. Reporting aspects cover test and run reporting, generating dashboards and HTML reports, capturing screenshots or video on failure, and integrating diagnostic output into continuous integration and monitoring pipelines. Emphasize tool selection, integration patterns, alerting on diagnostic signal, privacy and security considerations for logs and traces, and practices that make telemetry actionable for incident response and postmortem analysis.
Edge Case Identification and Testing
Focuses on systematically finding, reasoning about, and testing edge and corner cases to ensure the correctness and robustness of algorithms and code. Candidates should demonstrate how they clarify ambiguous requirements, enumerate problematic inputs such as empty or null values, single element and duplicate scenarios, negative and out of range values, off by one and boundary conditions, integer overflow and underflow, and very large inputs and scaling limits. Emphasize test driven thinking by mentally testing examples while coding, writing two to three concrete test cases before or after implementation, and creating unit and integration tests that exercise boundary conditions. Cover advanced test approaches when relevant such as property based testing and fuzz testing, techniques for reproducing and debugging edge case failures, and how optimizations or algorithmic changes preserve correctness. Interviewers look for a structured method to enumerate cases, prioritize based on likelihood and severity, and clearly communicate assumptions and test coverage.
Production Readiness and Professional Standards
Addresses the engineering expectations and practices that make software safe and reliable in production and reflect professional craftsmanship. Topics include writing production suitable code with robust error handling and graceful degradation, attention to performance and resource usage, secure and defensive coding practices, observability and logging strategies, release and rollback procedures, designing modular and testable components, selecting appropriate design patterns, ensuring maintainability and ease of review, deployment safety and automation, and mentoring others by modeling professional standards. At senior levels this also includes advocating for long term quality, reviewing designs, and establishing practices for low risk change in production.
Observability for Reliability and Capacity Planning
Using observability to design for reliability, handle failure modes, and plan capacity. Topics include golden signals and reliability metrics, SLOs and error budgets, failure mode analysis, graceful degradation and resiliency patterns, circuit breakers, timeouts and bulkheads, forecasting capacity needs, and how monitoring informs scaling and resource planning. Discusses tradeoffs for operating at scale, cost controls on telemetry, alert fatigue mitigation, and strategies for cascading failure prevention and recovery.
Reliability, SLO, and Error Budget Implications
Understand how architectural decisions affect reliability. For example, using a single database vs. replicated databases, synchronous vs. asynchronous processing. Discuss SLOs (e.g., 99.9% uptime) and what that means architecturally. Understand error budgets and how they influence rollout strategies or feature prioritization.
Edge Case Handling and Debugging
Covers the systematic identification, analysis, and mitigation of edge cases and failures across code and user flows. Topics include methodically enumerating boundary conditions and unusual inputs such as empty inputs, single elements, large inputs, duplicates, negative numbers, integer overflow, circular structures, and null values; writing defensive code with input validation, null checks, and guard clauses; designing and handling error states including network timeouts, permission denials, and form validation failures; creating clear actionable error messages and informative empty states for users; methodical debugging techniques to trace logic errors, reproduce failing cases, and fix root causes; and testing strategies to validate robustness before submission. Also includes communicating edge case reasoning to interviewers and demonstrating a structured troubleshooting process.
Testability and Testing Practices
Emphasizes designing code for testability and applying disciplined testing practices to ensure correctness and reduce regressions. Topics include writing modular code with clear seams for injection and mocking, unit tests and integration tests, test driven development, use of test doubles and mocking frameworks, distinguishing meaningful test coverage from superficial metrics, test independence and isolation, organizing and naming tests, test data management, reducing flakiness and enabling reliable parallel execution, scaling test frameworks and reporting, and integrating tests into continuous integration pipelines. Interviewers will probe how candidates make code testable, design meaningful test cases for edge conditions, and automate testing in the delivery flow.