Question 1
Difficulty: medium
How do you decide whether a feature should be built as a new microservice or kept inside an existing one?
Sample answer
I usually start by looking at the business capability, not the technology. If the feature has a clear domain boundary, its own data ownership, and a lifecycle that may change independently, I lean toward a new microservice. I also consider team ownership, deployment frequency, and whether the service would reduce coupling in the current system. On the other hand, if the feature is tightly related to an existing domain, needs the same data model, or would create a lot of cross-service coordination, I keep it in the current service until the boundary is clearer. I try to avoid creating services just because the architecture sounds modern. In practice, I’ve found that premature splitting can create more operational overhead than value. I prefer to align with DDD concepts, validate the boundary with product and engineering, and make sure the service will actually be independently deployable and maintainable before extracting it.
Question 2
Difficulty: hard
Tell me about a time you had to debug a production issue in a microservices environment.
Sample answer
In one project, we had intermittent order failures that only showed up under peak traffic. The challenge was that each service logs its own part of the flow, so the symptoms looked unrelated at first. I started by tracing the request path using correlation IDs across the API gateway, order service, payment service, and inventory service. That helped me see that the failures were not in the payment call itself but in a timeout caused by retries stacking up in the inventory service. The inventory service was making a synchronous call to a slow downstream dependency, and the retry policy was amplifying the problem. I worked with the team to shorten the timeout, reduce retries, and introduce circuit breaking. We also added better metrics around latency and error rates. The main lesson for me was that in microservices, the root cause is often in service interaction, not just in the service where the error appears.
Question 3
Difficulty: hard
How do you design communication between microservices to balance reliability and performance?
Sample answer
I choose the communication style based on the use case. For request-response flows where the user needs an immediate answer, I use synchronous APIs, but I keep them lightweight and resilient with timeouts, retries, and circuit breakers. For workflows that do not need an instant response, I prefer asynchronous messaging because it decouples services and improves resilience under load. I also pay close attention to idempotency, especially when events or messages may be delivered more than once. On the performance side, I try to avoid chatty service-to-service calls and unnecessary data transformation. Sometimes a service boundary is fine, but the API design is poor, so I optimize the contract before adding infrastructure complexity. I also make sure observability is part of the design, because reliability is hard to achieve if we cannot see latency, failures, and message backlog clearly. In my experience, the best architecture is usually a mix, not one pattern for everything.
Question 4
Difficulty: hard
What steps would you take to migrate a monolithic application to microservices?
Sample answer
I would treat it as a gradual refactoring effort, not a big rewrite. First I’d identify stable business domains and look for areas where the monolith has clear pain points, such as slow deployments, scaling bottlenecks, or frequent regression risk. Then I’d choose a low-risk service candidate, usually something with well-defined boundaries and limited dependencies. I’d use the strangler pattern so new functionality can move into the service while the monolith continues to run. Data migration is often the hardest part, so I’d plan that carefully, possibly starting with read-only replication or service-owned tables before fully cutting over. I’d also invest early in observability, automated testing, and deployment pipelines so each extracted service is supportable. Throughout the migration, I’d keep close communication with product and operations teams because trade-offs are inevitable. My goal would be to reduce risk in each step and prove value incrementally instead of trying to redesign everything at once.
Question 5
Difficulty: medium
How do you handle database design in a microservices architecture?
Sample answer
My first rule is that each service should own its data. That avoids hidden coupling and lets teams evolve independently. I prefer separate databases or at least clearly separated schemas where ownership is enforced. I’m careful about not sharing tables across services, because that usually becomes a source of tight coupling and deployment friction. When one service needs data owned by another, I think in terms of API calls, events, or replicated read models rather than direct database access. For reporting or cross-service queries, I often use event-driven data propagation into a search index, analytics store, or materialized view. I also design for eventual consistency when appropriate, since strict consistency across services can make the system brittle. The challenge is balancing autonomy with practical business needs, so I always make sure product stakeholders understand when data may be slightly delayed. In my experience, strong data boundaries are one of the biggest factors in whether a microservices system stays maintainable.
Question 6
Difficulty: medium
Describe a situation where you disagreed with an architecture decision and how you handled it.
Sample answer
I once worked on a project where the initial plan was to introduce a large number of microservices very early, before the domain boundaries were fully understood. I was concerned that the team would spend more time on infrastructure and inter-service coordination than on delivering business value. Instead of arguing abstractly, I prepared a concrete comparison showing the operational costs, deployment overhead, and failure scenarios we would introduce. I also proposed a phased approach: start with a modular monolith for the core domain, then extract services where the boundaries became obvious. During the discussion, I focused on business goals rather than just technical preference. That helped keep the conversation constructive. We ultimately agreed to extract only the areas that had clear autonomy needs. I think the key is to disagree respectfully, bring evidence, and stay open to compromise. The goal is not to “win” the architecture debate; it is to build something that works for the team and the business.
Question 7
Difficulty: medium
How do you ensure your microservices are secure?
Sample answer
I approach security as part of the design, not as a final checklist. At the service level, I make sure every endpoint has authentication and authorization appropriate to the resource and action. I prefer centralized identity management with token-based access, and I validate claims carefully inside each service. For service-to-service communication, I use secure transport and, where needed, service identities so internal traffic is not treated as implicitly trusted. I also minimize sensitive data exposure by sending only what the service actually needs. Logging is another area I watch closely because teams often accidentally log credentials or personal data during debugging. From a development perspective, I support dependency scanning, secret management, and regular patching as part of the delivery pipeline. I also like to review security concerns early with the platform or security team so we are not discovering issues late in the release cycle. In microservices, a small weakness can spread quickly, so consistency matters a lot.
Question 8
Difficulty: hard
What do you do when a service becomes a bottleneck under high load?
Sample answer
I start by measuring before changing anything. I want to know whether the bottleneck is CPU, memory, database latency, thread starvation, network overhead, or an external dependency. Once I have that data, I look for the simplest fix with the highest impact. Sometimes it’s a code optimization, but often it’s about reducing unnecessary calls, adding caching, or changing a synchronous interaction into an asynchronous one. If the service is stateless and the load is valid, horizontal scaling may be the right answer, but I would not scale blindly if the real issue is inefficient design. I also look at downstream services, because one slow dependency can make a healthy service look overloaded. In one case, we improved throughput by batching requests and introducing backpressure rather than just adding more instances. I try to keep the focus on system behavior, not just the individual service. The best fix is the one that improves resilience without hiding deeper design issues.
Question 9
Difficulty: medium
How do you write tests for microservices without making the test suite too slow or fragile?
Sample answer
I try to build a layered test strategy. At the base, I want fast unit tests for the business logic, validation rules, and edge cases. Then I add service-level tests that cover API behavior and important data transformations. For interactions between services, I prefer contract tests because they catch breaking changes without requiring full end-to-end environments for every scenario. I still use a small number of end-to-end tests, but only for the most critical paths, since those are slower and more brittle. I also pay attention to test data setup, because poor fixtures can make tests hard to maintain. Mocking is useful, but I avoid mocking so much that the test no longer resembles real behavior. In microservices, the trick is to test the boundaries that matter most while keeping feedback fast. My goal is to give the team confidence to deploy frequently without turning the pipeline into a bottleneck or a source of false alarms.
Question 10
Difficulty: easy
How do you stay effective when working with multiple microservices, different teams, and frequent deployments?
Sample answer
I stay effective by being very intentional about communication and ownership. In a microservices environment, it is easy for work to become fragmented, so I make sure I understand which service owns which behavior and who is responsible for each dependency. I keep documentation concise but current, especially around API contracts, event schemas, and operational runbooks. I also like to use shared observability dashboards so teams are looking at the same signals when issues happen. When I’m planning work, I think about the downstream effects of a change, not just the code in front of me. If a deployment could impact another team, I give them early notice and align on testing or rollout strategy. I’ve found that frequent deployments are manageable when the team has strong automation, clear interfaces, and a habit of surfacing risks early. I don’t see coordination as overhead; I see it as part of building a system that can evolve safely.
