Choose a project where you owned the architecture and can clearly articulate the problem, constraints, and trade-offs. Structure your answer as a narrative: problem, constraints, options considered, decision, and outcome. Emphasize how constraints shaped your architecture and what you learned.
Pro tip: Quantify the impact of your architecture (e.g., latency reduction, cost savings) and briefly mention what you would do differently now to show growth and self-awareness.
Briefly describe the project's purpose, your role, and the team size to orient the interviewer. Keep it concise to leave time for the technical details.
Clearly state the problem you were solving and the key constraints (e.g., scalability, latency, budget, legacy systems, compliance). Explain why these constraints mattered.
Outline 2-3 architectural approaches you considered, highlighting their pros and cons relative to the constraints. Show that you evaluated alternatives systematically.
Describe the chosen architecture in enough detail to demonstrate technical depth, and justify why it best satisfied the constraints. Mention any key technologies or patterns used.
Summarize the results (metrics, impact) and reflect on what you learned or would change. This shows maturity and continuous improvement.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
They want to see you actually evaluated other paths, not just post-hoc rationalized the one you took.
Choose a real technical decision you made where multiple viable options existed, and walk through your evaluation process. Focus on the criteria you used to compare alternatives and why the rejected options didn't meet your needs. Be honest about trade-offs and show that you considered non-obvious factors like maintainability and team expertise.
Pro tip: Don't just list alternatives—explain the specific constraints (e.g., latency, cost, team skills) that made them unsuitable, and acknowledge any merits they had. This shows you evaluate holistically rather than dogmatically.
Briefly describe the problem or project and the key requirements that any solution had to meet. This grounds your decision in real constraints.
Name 2-3 serious alternatives you considered, including the one you ultimately chose. Avoid strawman options that no one would pick.
For each alternative, explain how it performed against your key criteria (e.g., performance, scalability, development speed, operational complexity). Use concrete examples or data if possible.
Clearly state why each rejected alternative fell short—whether due to a dealbreaker constraint or because another option was significantly better overall.
Briefly mention the result of your choice and any lessons learned, showing you can learn from past decisions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Straightforward but easy to botch if you're too vague.
Use the STAR method to describe a specific project, clearly delineating your individual contributions from the team's collective work. Emphasize how your actions complemented the team's efforts and contributed to shared goals, while being precise about what you personally did.
Pro tip: Avoid taking credit for team achievements; instead, highlight your specific role and how it enabled the team's success. This demonstrates humility and collaboration, which are highly valued at companies like Abridge.
Briefly describe the project, its goals, and the team composition to provide necessary background.
Summarize the team's overall approach and key deliverables, acknowledging the collective effort.
Clearly state your individual tasks, decisions, and impact, using 'I' statements to distinguish your work.
Explain how you worked with others, supported team members, and aligned your work with the team's objectives.
Conclude with the project's outcomes and how your contributions helped achieve them, reinforcing the value of both individual and team efforts.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Structure your answer as a concise story: start with the system's purpose and the rollout strategy (e.g., phased, canary, feature flags), then describe the key metrics you tracked and the results you observed. Emphasize how you used data to validate success and make decisions, and conclude with lessons learned or next steps.
Pro tip: Quantify the impact with specific before-and-after metrics (e.g., latency reduced by 30%, error rate dropped from 2% to 0.5%) and mention how you ensured a safe rollout (e.g., gradual traffic increase, rollback plan). This shows you think like an owner and understand both engineering and product outcomes.
Briefly describe the system, its purpose, and the problem it solved. State the key success metrics you aimed to improve.
Explain your rollout approach: phased, canary, blue-green, feature flags, etc. Mention how you minimized risk and ensured a smooth deployment.
List the specific metrics you tracked (e.g., latency, error rate, adoption, conversion) and the tools you used to monitor them.
Present the observed results with quantitative data. Compare before and after, and highlight any unexpected findings.
Summarize what you learned, how you iterated, and any follow-up actions or improvements made based on the results.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the current scale and the specific bottlenecks, then systematically analyze how each component behaves as load increases by 10x and 100x. Focus on identifying which parts break first and propose concrete architectural changes to address them, while discussing trade-offs.
Pro tip: Quantify the impact: estimate how metrics like latency, throughput, and cost change with scale, and show that you prioritize fixes based on business impact. This demonstrates a pragmatic, data-driven approach that senior engineers value.
Ask questions to understand the current load (e.g., QPS, data volume, user count) and the expected growth pattern. State any assumptions you make about the workload characteristics.
Analyze each component (e.g., database, application servers, cache, network) to determine which will hit limits first as load increases. Consider both resource saturation and architectural constraints.
For each bottleneck, suggest specific techniques such as horizontal scaling, sharding, caching, asynchronous processing, or using managed services. Explain how these address the issue.
Acknowledge the costs, complexity, and potential downsides of each proposed change. Compare alternatives and justify your recommendations based on requirements like consistency, latency, and budget.
Conclude with a prioritized plan, highlighting which changes are most critical and how you would validate them (e.g., through load testing or incremental rollouts).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one tripped me up more than I expected.
Start by clarifying the system's scope and critical user journeys, then systematically walk through failure modes across layers (client, network, service, data, dependencies). For each, describe detection mechanisms (metrics, logs, traces, alerts) and recovery strategies (retries, fallbacks, circuit breakers, graceful degradation), prioritizing based on impact and likelihood.
Pro tip: Tie failure modes to business impact—e.g., a transcription service outage delays clinical notes, affecting patient care—and propose detection that catches issues before users notice, like synthetic checks or anomaly detection on key metrics.
Ask clarifying questions to understand the system's boundaries, dependencies, and most critical user journeys. This ensures your analysis focuses on what matters most.
Systematically identify potential failures across client, network, application, data, and external dependencies. Consider hardware, software, and human errors.
Assess each failure mode's potential business impact and probability, then prioritize the ones to address first. Use a risk matrix or similar approach.
For each prioritized failure, specify how you would detect it: monitoring, logging, tracing, alerting, health checks, synthetic transactions, etc.
Outline recovery approaches such as retries with backoff, circuit breakers, fallbacks, graceful degradation, failover, and automated remediation. Include testing and validation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Weirdly the question I felt most comfortable with.
Choose a specific technical decision you made, explain the context and trade-offs, then describe what you would change and why. Focus on the learning and how it improved your decision-making, showing self-awareness and growth.
Pro tip: Frame your change as a refinement, not a regret—emphasize that the original decision was reasonable given the information at the time, but you now have new insights. This shows maturity and avoids sounding defensive.
Briefly describe the project, your role, and the specific decision you made. Keep it concise to focus on the reflection.
State what you decided and why it seemed like the best choice at the time, including constraints or trade-offs you considered.
Clearly state what you would do differently now and the specific reasons—e.g., new information, better alternatives, or unforeseen consequences.
Explain what you learned from this experience and how it has influenced your subsequent decisions or approach.
Relate the learning to the skills and mindset needed for the position, showing how you apply lessons to drive better outcomes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.