Your Guide to the Top 10 Microservices Interview Questions (with Answers)

Microservices architecture has become the de facto standard for designing scalable, resilient, and sustainable software systems. Organizations from startups to enterprises are quickly moving away from monoliths to microservices. This transition is creating a demand for developers who understand not just the what but also the why and how of microservices.

This architectural style of designing and developing software applications involves dividing an application into various, independent services that are separate and distinct from each other, with each one developed, deployed, and maintained independently. Although there are numerous advantages of using microservices, there are a few drawbacks that need to be taken into account.

Whether you are a fresher developer looking to enter backend development or an experienced developer gearing up for a senior position, learning microservices is essential to differentiate yourself during interviews and contribute positively to contemporary software teams.

Here’s what we’re going to cover in this guide:

Top 10 Microservices interview questions and answers with a focus on experienced professionals

3 easy-to-understand questions and answers to help beginners enter microservices

Tips and best practices sprinkled throughout to give you an edge in real-world interviews

Let’s get started.

Top 10 Microservices Interview Questions and Answers

1. How do you handle communication between microservices?

Answer: There are two main ways:  synchronous (usually by HTTP REST or gRPC) and asynchronous (by messaging queues like RabbitMQ, Kafka, or AWS SNS/SQS).

• Use REST/gRPC when a real-time response is required.
• Use event-driven messaging for decoupling and fault-tolerance.

Here is a tip: In interviews, demonstrate knowledge of event sourcing and CQRS since they are gaining popularity in distributed systems.

2. What database management techniques do you employ in microservices?

Answer: Ideally, every microservice should maintain its database (Database per Service pattern) to achieve loose coupling and data encapsulation. 

For consistency, apply:

• Sagas for handling distributed transactions
• Eventual consistency as a guiding principle
• Change Data Capture (CDC) for synchronizing among services

Tools: Debezium (for CDC), Kafka (for event streaming)

3. How do you make your microservices resilient and fault-tolerant?

Fault tolerance ensures that the system remains functional at all times, enhancing user experience. It also makes things much easier for software teams. 

Answer: Resilience patterns are:

• Circuit Breaker (e.g., Netflix Hystrix, Resilience4j)
• Retry with Backoff
• Bulkheads and Rate Limiting

Example: When Service A relies on Service B, and B is not available, a Circuit Breaker avoids A getting flooded with failed calls and may resort to fallback logic.

4. How do you design and deploy microservices securely?

Answer: Important practices for microservices secure deployment are:

• OAuth2 / JWT for stateless authentication
• Mutual TLS for service-to-service encryption
• API Gateway to secure, rate-limit, and route centrally
• Secrets Management with Vault or AWS Secrets Manager

Here is a tip: Display awareness of zero trust architecture and securing internal APIs, not only external-facing ones.

5. How do you log and monitor across microservices?

Answer: To log and monitor across each microservice, follow these steps. 

• Use centralized logging: ELK Stack (Elasticsearch, Logstash, Kibana) or EFK (Fluentd)

• Use distributed tracing tools such as Jaeger or Zipkin
• Integrate with monitoring tools: Prometheus + Grafana

Here is a tip: Describe the usage of correlation IDs between services to follow end-to-end requests.

6. What are microservices’ challenges, and how do you address them?

Answer: While microservices offer flexibility and modularity, they are not free from challenges. Development teams often face many challenges that might include:

• Data consistency → Utilize event-driven architecture
• Deployment complexity → Implement CI/CD Pipelines and Kubernetes
• Service discovery → Utilize tools such as Consul, Eureka, or K8s DNS
• Versioning → Address through backward-compatible API design or versioned endpoints

7. How do you configure microservices?

Answer: Configuring microservices architecture may include managing and maintaining the settings that control the behavior of each microservice. These settings can include database connections, API keys, feature toggles, and environment-specific configurations. Here are some best practices:

• Utilize centralized config servers (e.g., Spring Cloud Config)
• Use environment-specific configurations using tools such as Kubernetes ConfigMaps and Secrets
• Make configurations immutable in production

Here is a tip: Talk about feature toggles and dynamic configuration reload mechanisms.

8. Describe the role of the API Gateway in microservices?

Answer: An API Gateway is a single entry point to all clients, and offers:

• Routing to suitable services
• Authentication & Authorization
• Rate limiting & Throttling
• Load balancing and Caching

Popular options: Kong, NGINX, AWS API Gateway, Istio (in service mesh)

9. How do you deploy microservices into production?

Answer: Deploying microservices into production typically involves several key steps and considerations to ensure reliability, scalability, and maintainability. You can deploy using container orchestration tools such as Kubernetes or Docker Swarm.

Best practices:

• Blue-Green or Canary Deployments
• Health checks & readiness probes
• Automated rollbacks on failure
• Observability baked into the CI/CD pipeline

10. What is a service mesh, and when do I use one?

Answer: A service mesh is an infrastructure layer that governs service-to-service communication in microservices systems. It takes care of functions such as traffic routing, security, observability, and resiliency, hiding these complexities from individual services. You employ a service mesh when you’re working with a distributed application with lots of microservices and want to handle their interactions effectively and reliably.

(e.g., Istio, Linkerd) is an infrastructure layer that manages:

• Service discovery
• Traffic management
• Security (mTLS)
• Observability (telemetry, tracing)

Use case: When you have lots of services talking to each other internally and want to have uniform governance, security, and resilience without adding additional code in every service.

Here are some Q&A for freshers:

Microservices Questions and Answers for Freshers

1. What is microservices architecture?

Answer: Microservices architecture refers to the design of software as a set of small, stand-alone, deployable services. Every service addresses one business capability, exchanges messages across the network, and can be developed and scaled separately.

2. What are the major benefits of microservices?

Answer: Microservices provide many benefits centered around key areas such as greater agility, shorter development cycles, better scalability, and more robust fault isolation. These are derived from decomposing applications into smaller independent services that can be developed, deployed, and scaled independently.

Major benefits are:

• Scalability: Scale individual services on demand
• Flexibility: Employ various tech stacks per service
• Faster deployments: Independent teams
• Resilience: One service failure doesn’t bring down the entire application

3. How do microservices contrast with monolithic architecture?

Answer: Microservices and monolithic designs are fundamentally different design methodologies for constructing software applications. Monolithic design consists of one, tightly bound codebase, whereas microservices decompose applications into smaller, independent, and loosely coupled services. This difference results in drastic differences in development, deployment, scaling, and fault tolerance.

Aspect	Monolith	Microservices
Deployment	Single unit	Individual services
Scaling	Whole app	Per service
Development	Tightly coupled	Loosely coupled
Technology choice	Uniform	Polyglot possible

Tip for Freshers: Understand the progression from monolith → SOA → microservices.

Bonus Tips for Interview Success

Use real-world experience when answering questions, even small-scale microservices projects or side projects.

Trade-offs: When not to use Microservices

Microservices aren’t a silver bullet. You should know when not to use them. Here is the complete list of reasons when you should not use Microservices Architectures: 

Here are the Anti-Patterns of Microservices:

Don’t do Distributed Monolith

Ensure that you break your services down correctly and adhere to the decoupling principle, such as using bounded context and business capabilities principles.

Don’t implement microservices without DevOps or cloud services

Microservices adopt the distributed cloud-native patterns. And you can only reap the benefits of microservices by adhering to the following cloud-native principles:

Some of them are:

• CI/CD pipeline with DevOps automations
• Correct deployment and monitoring tools
• Managed cloud services to back your infrastructure
• Follow Key enabling technologies and tools such as Containers, Docker, and Kubernetes.
• Broken dependencies with the following async communications using Messaging and event streaming services.

When having limited team sizes, small Teams

If you do not have a team size that can manage the microservice workloads, this would only lead to a delay in delivery. For a small team, a microservice architecture can be difficult to justify, since the team is needed just to deal with the deployment and management of the microservices themselves.

Launching Brand new products or with startups

If you are building a new startup or a completely new product that needs deep change when you build and keep iterating your product, then you should not begin with microservices.

AI Engineering in 2025: Skills, Tools, and Paths to Success

Agentic AI and other buzzwords are emerging almost monthly if not more often. In reality they all describe different variations of Agentic Systems, it might be n agentic workflow or multi-agent system, it’s just a different topology under the same umbrella.

If you are considering a career in AI Engineering in 2025, it might feel overwhelming and that is completely normal.

But you need to remember - you are not too late to the game. The role as such has only emerged over the past few years and is still rapidly evolving.

In order to excel in this competitive space, you will need a clear path and focused skills.

Here is a roadmap you should follow if you want to excel as an AI Engineer in today’s landscape.

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Fundamentals - learn as you go.

I have always been a believer that learning fundamentals is key to your career growth. This has not changed.

However, I have to admit that the game itself has changed with the speed that the industry is moving forward. Staring of with fundamentals before anything else is no longer an option. Hence, you should be continuously learning them as you build out modern AI Engineering skillset.

Here is a list of concepts and technologies I would be learning and applying in my day-to-day if I were to start fresh.

The Fundamentals.

Python and Bash:

• FastAPI - almost all of the backed services implemented in Python are now running as FastAPI servers.

• Pydantic - the go to framework for data type validation. It is now also a Python standard for implementing structured outputs in LLM based applications.

• uv - the next generation Python package manager. I haven’t seen any new projects not using it.

• git - get your software version control fundamentals right.

• Asynchronous programming - extremely important in LLM based applications as your Agentic topologies will often benefit from calling multiple LLM APIs asynchronously without blocking.

• Learn how to wrap your applications into CLI tools that can be then executed as CLI scripts.

Statistics and Machine Learning:

• Understand the non-deterministic nature of Statistical models.

• Types of Machine Learning models - it will help you when LLMs are not the best fit to solve non-deterministic problem.

• General knowledge in statistics will help you in evaluating LLM based systems.

• Don’t get into the trap of thinking that AI Engineering is just Software Engineering with LLMs, some maths and statistics is involved.

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LLM and GenAI APIs.

You should start simple, before picking up any LLM Orchestration Framework begin with native client libraries. The most popular is naturally OpenAI’s client, but don’t disregard Google’s genai library, it is not compatible with OpenAI APIs but you will find use cases for Gemini models for sure.

So what should you learn?

LLM APIs.

Types of LLMs:

• Foundation vs. Fine-tuned.

• Code, conversational, medical etc.

• Reasoning Models.

• Multi-Modal Models.

Structured outputs:

• Learn how OpenAI and Claude enforces structured outputs via function calling and tool use.

• Try out simple abstraction libraries like Instructor - they are enough for most of the use cases and uses pydantic for the structure definition natively.

Prompt Caching:

• Learn how KV caching helps in reducing generation latency and costs.

• Native prompt caching provided by LLM providers.

• How LLM serving frameworks implement it in their APIs (e.g. vLLM).

Model Adaptation.

I love the term Model Adaptation. The first time (and maybe the only time) I’ve seen it in literature was in the book “AI Engineering” by 

Chip Huyen

. The term ideally encompasses what we, AI Engineers, do to make LLMs perform actions we expect.

What should you learn?

Model Adaptation.

Prompt Engineering:

• Learn the proper prompt structure. It will differ depending on the provider you are using.

• Understand context size limitations.

• Prompting techniques like Chain of Thought, Tree of Thought, Few-shot.

• Advanced prompting techniques: Self-consistency, Reflection, ReAct.

Tool Use:

• Tool Use is not magic, learn how it is implemented via context manipulation.

• Don’t rush to agents yet, learn how LLMs are augmented with tools first.

• You might want to pick up a simple LLM Orchestrator Framework at this stage.

Storage and Retrieval.

Storage and Retrieval.

Vector Databases:

• Learn strengths and weaknesses of vector similarity search.

• Different types of Vector DB indexes: Flat, IVFFlat, HNSW.

• When PostgreSQL pgvector is enough.

Graph Databases:

• High level understanding about Graph Databases.

• Don’t spend too much time here as there is still limited use for Graph DBs even though the promises connected with Graph Retrieval were and still are big.

• Current challenges still revolve around the cost of data preparation for Graph Databases.

Hybrid retrieval:

• Learn how to combine the best from keyword and semantic retrieval to get the most accurate results.

RAG and Agentic RAG.

RAG and Agentic RAG.

Data Preprocessing:

• Learn data clean data before computing Embeddings.

• Different chunking strategies.

• Extracting useful metadata to be stored next to the embeddings.

• Advanced techniques like Contextual Embeddings.

Data Retrieval, Generation and Reranking:

• Experiment with amount of data being retrieved.

• Query rewriting strategies.

• Prompting for Generation with retrieved Context.

• Learn how reranking of retrieved results can improve the accuracy of retrieval in your RAG and Agentic RAG systems.

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MCP:

• Agentic RAG is where MCP starts to play a role, you can implement different data sources behind MCP Servers. By doing so you decouple the domain responsibility of the data owner.

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LLM Orchestration Frameworks:

• You don’t need to rush with choosing Orchestration Framework, most of them hide the low level implementation from you and you would be better off starting out without any Framework whatsoever and using light wrappers like Instructor instead.

• Once you want to pick up and Orchestrator, I would go for the popular ones because that is what you run into in the real world:

  • LangChain/LangGraph.

  • CrewAI.

  • LlamaIndex

  • Test out Agent SDKs of Hyper-scalers and AI Labs.

AI Agents.

AI Agents.

AI Agent and Multi-Agent Design Patterns:

• ReAct.

• Task Decomposition.

• Reflexion.

• Planner-Executor.

• Critic-Actor.

• Hierarchical.

• Collaborative.

• …

Memory:

• Learn about Long and Short-Term memory in Agentic Systems and how to implement it in real world.

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• Try out mem0 - the leading Framework in the industry for managing memory. It now also has an MCP server that you can plug into your agents.

Human in or on the loop:

• Learn hoe to delegate certain actions back to humans if the Agent is not capable to solve the problem or the problem is too sensitive.

• Human in the loop - a human is always responsible for confirming or performing certain actions.

• Human on the loop - the Agent decides if human intervention is needed.

A2A, ACP, etc.:

• Start learning Agent Communication Protocols like A2A by google or ACP by IBM.

• There are more Protocols popping out each week, but the idea is the same.

• Internet of Agents is becoming a real thing. Agents are implemented by different companies or teams and they will need to be able to communicate with each other in a distributed fashion.

Agent Orchestration Frameworks:

• Put more focus on Agent Orchestration Frameworks defined in the previous section.

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Infrastructure.

Infrastructure.

Kubernetes:

• Have at least basic understanding of Docker and Kubernetes.

• If your current company does not use K8s, it is more likely you will run into the one that does use it rather than the opposite.

Cloud Services:

• Each of the major cloud providers have their own set of services meant to help AI builders:

  • Azure AI Studio.

  • Google Vertex AI.

  • AWS Bedrock.

CI/CD:

• Learn how to implement Evaluation checks into your CI/CD pipelines.

• Understand how Unit Eval Tests are different from Regression Eval Tests.

• Load test your applications.

Model Routing:

• Learn how to implement Model fallback strategies to make your

• Try tools like liteLLM, Orq or Martian.

LLM Deployment:

• Learn basics of LLM deployment Frameworks like vLLM.

• Don’t focus too much on this as it would be a rare case that you would need to deploy your own models in real world.

Observability and Evaluation.

Observability and Evaluation.

AI Agent Instrumentation:

• Learn what SDKs exist for instrumenting Agentic applications, some examples:

  • Langsmith SDK.

  • Opik SDK.

  • Openllmetry.

  • …

• Learn Multi-Agent system Instrumentation. How do we connect traces from multiple agents into a single thread.

• You can also dig deeper into OpenTelemetry because most of the modern LLM Instrumentation SDKs are built on top of it.

Observability Platforms:

• There are many Observability platforms available off the shelf, but you nee to learn the fundamentals of LLM Observability:

  • Traces and Spans.

  • Evaluation datasets.

  • Experimenting with changes to your application.

  • Sampling Traces.

  • Prompt versioning and monitoring.

  • Alerting.

  • Feedback collection.

  • Annotation.

Evaluation Techniques:

• Understand the costs associated with LLM-as-a-judge based evaluations:

  • Latency related.

  • Monetary related.

• Know in which step of the pipeline you should be running evaluations to get most out of it. You will not be able to evaluate every run in production due to cost constraints.

• Learn alternatives to LLM based evaluation:

  • Rule based.

  • Regex based.

  • Regular Statistical measures.

Recently, I wrote a piece on building and evolving your Agentic Systems. The ideas I put out are very tightly connected with being able to Observe and Evaluate your systems as they are being built out. Read more here:

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Security.

Security.

Guardrails:

• Learn how to guardrail inputs to and outputs from the LLM calls.

• Different strategies:

  • LLM based checks.

  • Deterministic rules (e.g. Regex based).

• Try out tools like GuardrailsAI.

Testing LLM based applications:

• Learn how to test the security of your applications.

• Try to break your own Guardrails and jailbreak from system prompt instructions.

• Performing advanced Red Teaming to test emerging attack strategies and vectors.

Looking Forward.

The future development of Agents will be an interesting area to observe. A lot of successful startups are most likely to succeed due to having one of the following:

• Distribution.

• Good UX.

• Real competitive motes, like physical products. Here is where robotics comes into play.

Looking Forward Elements.

Voice, Vision and Robotics:

• An interesting blend of capabilities that would allow a physical machine to interact with the world. The areas that I am looking forward to are:

  • On-device Agents.

  • Extreme Quantisation techniques.

  • Foundation Models tuned specifically for robotics purposes.

Automated Prompt Engineering:

• New techniques are emerging that allow you to perform automated Prompt Engineering given that you have good test datasets ready for evaluation purposes.

• Play around with frameworks like DsPy or AdalFlow.

Summary.

The skillset requirements for AI Engineers are becoming larger every month. The truth is that in your day-to-day you will only need a subset of it.

You should always start with your immediate challenges and adapt the roadmap accordingly.

However, don’t forget to look back and learn the fundamental techniques that power more advanced systems. In many cases these fundamentals are hidden behind layers of abstraction.

AI Engineering Roadmap.

Happy building!