Top Solution Architect Interview Questions & Answers - Part II

 Top Solution Architect Interview Questions & Answers

.NET and Cloud Technologies (Azure)

Q1: Can you explain the key differences between .NET Framework and .NET Core?

Answer:

• .NET Framework is Windows-only and primarily used for enterprise applications.
• .NET Core is cross-platform, lightweight, and optimized for microservices and cloud-based applications.
• .NET Core has better performance, container support, and modular architecture using NuGet packages.

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Q2: What are Azure Functions, and how do they work?

Answer:

• Azure Functions is a serverless compute service that allows running event-driven code without managing infrastructure.
• It supports various triggers (HTTP, Timer, Queue, Event Grid, etc.) to execute logic.
• It scales automatically based on demand and supports multiple runtimes, including .NET, Node.js, Python, and Java.

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Q3: What are Azure Service Bus and Event Grid? When would you use each?

Answer:

• Azure Service Bus is a message broker that provides asynchronous messaging between applications using queues and topics. Ideal for decoupling microservices.
• Azure Event Grid is an event routing service that pushes events in real-time (e.g., resource creation/deletion notifications).
• Use Service Bus when message ordering and reliability are crucial, while Event Grid is suitable for event-driven architectures.

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Designing Scalable Systems

Q4: How do you design a scalable distributed system?

Answer:

• Use Microservices architecture to break monolithic applications.
• Implement Load Balancers (Azure Load Balancer, Azure Application Gateway) to distribute traffic.
• Utilize Caching mechanisms (Redis, Azure Cache for Redis) for frequently accessed data.
• Use Asynchronous messaging (Azure Service Bus, Kafka) to decouple services.
• Ensure Auto-scaling of resources based on demand.

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Q5: What are the key considerations when designing a microservices-based architecture?

Answer:

1. Service Boundaries: Define clear business functions for each microservice.
2. Database per Service: Avoid direct database sharing; use event-driven architecture if needed.
3. Communication: Use RESTful APIs, gRPC, or messaging queues for service communication.
4. Security: Implement OAuth2.0/OpenID Connect for authentication and API Gateway for centralized access.
5. Observability: Use logging (Serilog, ELK), monitoring (Application Insights, Prometheus, Grafana).

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Security & Authentication

Q6: What is the difference between OAuth 2.0 and OpenID Connect?

Answer:

• OAuth 2.0 is an authorization protocol that allows third-party apps to access user data without revealing credentials.
• OpenID Connect (OIDC) is built on OAuth 2.0 but provides authentication (identity verification).
• OAuth 2.0 issues Access Tokens (for API access), while OpenID Connect issues ID Tokens (for authentication).

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Q7: How do you secure APIs using OAuth 2.0?

Answer:

• Use Azure AD or Identity Server to issue JWT access tokens.
• Implement scopes and roles to control API access.
• Use API Gateway (Azure API Management) to enforce security policies.
• Store and validate tokens securely using OAuth flows (Client Credentials, Authorization Code, Implicit, PKCE).

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Microservices & Communication

Q8: What are the different ways microservices can communicate?

Answer:

1. Synchronous Communication:
   • REST APIs (HTTP-based)
   • gRPC (Binary, faster than REST)
2. Asynchronous Communication:
   • Message Brokers (Azure Service Bus, RabbitMQ, Kafka)
   • Event-driven architecture using Azure Event Grid
3. API Gateway (Azure API Management, Ocelot) for centralized management.

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Database & ORM

Q9: How does Entity Framework work, and what are its advantages?

Answer:

• Entity Framework (EF) is an ORM (Object-Relational Mapper) that simplifies database access in .NET.
• Benefits:
  • Code First / Database First approach.
  • LINQ queries instead of raw SQL.
  • Supports transactions, lazy loading, eager loading.
  • Works well with SQL Server, MySQL, PostgreSQL.

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Q10: What are the different ways to improve database performance in .NET applications?

Answer:

1. Use Caching (Redis, In-memory, Azure Cache for Redis).
2. Optimize Queries (Use indexes, avoid SELECT *).
3. Use Stored Procedures to reduce query execution time.
4. Implement Connection Pooling for database connections.
5. Use Asynchronous Calls (async/await with DbContext).
6. Partitioning & Sharding for large datasets.

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RESTful APIs & Integration

Q11: How do you design a RESTful API?

Answer:

1. Use Proper HTTP Methods:
   • GET (Read), POST (Create), PUT/PATCH (Update), DELETE (Remove).
2. Use Meaningful URIs: /api/orders/{id}/items instead of /getOrderItems.
3. Implement HATEOAS (Hypermedia As The Engine Of Application State) for discoverability.
4. Version APIs using /v1/orders or Accept: application/vnd.company.v1+json.
5. Secure APIs using OAuth 2.0, API Gateway, and rate limiting.

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Q12: What are the common HTTP status codes used in REST APIs?

Answer:

• 200 OK – Success
• 201 Created – Resource Created
• 204 No Content – Successful request, no response body
• 400 Bad Request – Invalid input
• 401 Unauthorized – Authentication required
• 403 Forbidden – Not enough permissions
• 404 Not Found – Resource not found
• 500 Internal Server Error – Server failure

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Monitoring & Observability

Q13: How do you monitor and debug cloud applications?

Answer:

• Application Insights for real-time logging.
• Azure Monitor, Log Analytics for analyzing logs.
• Distributed Tracing (OpenTelemetry, Jaeger, Zipkin) for microservices.
• Alerts and Dashboards (Grafana, Prometheus) to monitor system health.
• Dead-letter queues in Azure Service Bus to track failed messages.

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Customer Proposals & Solution Design

Q14: What are key aspects of writing a customer proposal for a software solution?

Answer:

1. Understanding Customer Requirements – Gather functional and non-functional requirements.
2. Solution Architecture – Define high-level architecture, technology stack, and integrations.
3. Security & Compliance – Address authentication, authorization, and data protection measures.
4. Scalability & Performance – Ensure the system meets business growth needs.
5. Cost Estimation & Timeline – Provide budget-friendly solutions with a clear roadmap.
6. Risk Management – Identify potential risks and mitigation strategies.

Can you walk us through a complex project where you leveraged Azure OpenAI, LangChain, embedding models, and the Milvus Vector database to streamline a business process? Specifically, how did you address the challenges you faced during the project, and what were the key results and impact?

Let's structure your response using the STAR method (Situation, Task, Action, Result) for your project:

Situation:

Our team was tasked with developing an application to streamline the review process of medical plans. The goal was to provide a tool that would enable leadership to assess findings and recommendations efficiently. The existing process was manual and time-consuming, leading to inefficiencies and delays.

Task:

My responsibility was to design and implement a solution that would automate and optimize the medical plan review process. This involved leveraging Azure OpenAI, LangChain, embedding models, and the Milvus Vector database to create a robust and efficient system.

Action:

1. Requirement Analysis: I collaborated with stakeholders to understand their needs and define the project requirements.

2. Technology Selection: I chose Azure OpenAI for its advanced natural language processing capabilities, LangChain for its seamless integration, and the Milvus Vector database for efficient data indexing and retrieval.

3. Architecture Design: I designed the system architecture, ensuring scalability, security, and performance. The architecture included microservices for handling different components, such as data ingestion, processing, and reporting.

4. Implementation: I developed the core components using .NET 8 and integrated Azure OpenAI for NLP tasks. LangChain was used for orchestrating the workflow, and Milvus Vector database was implemented for fast and accurate data retrieval.

5. Testing and Validation: I conducted rigorous testing to ensure the system met performance and accuracy requirements. I also organized user acceptance testing (UAT) sessions with stakeholders to gather feedback and make necessary adjustments.

6. Deployment and Training: I deployed the solution to Azure and conducted training sessions for the leadership team to ensure they could effectively use the application.

Result:

The new application significantly streamlined the medical plan review process. Key achievements included:

• Efficiency Improvement: Reduced review time by 75%, allowing leadership to make faster and more informed decisions.

• Accuracy Enhancement: Improved the accuracy of findings and recommendations through advanced NLP and embedding models.

• User Satisfaction: Received positive feedback from leadership and stakeholders for its user-friendly interface and robust performance.

By leveraging cutting-edge technologies and following a structured approach, we successfully delivered a solution that met the project goals and exceeded stakeholder expectations.