Real-time API testing interview questions and answers tailored for experienced candidates. The first set includes fundamental questions (1 to 10), while the subsequent set covers scenario-based questions (11 to 25) to assess practical skills.
API testing involves evaluating the functionality, performance, security, and reliability of Application Programming Interfaces (APIs). APIs allow different software systems to communicate and interact seamlessly.
Here’s a list of some common HTTP status codes:
200 OK: The request was successful, and the server returned the requested data.
201 Created: The request resulted in the creation of a new resource on the server.
204 No Content: The server successfully processed the request but has no data to send in response.
400 Bad Request: The server could not understand the request due to malformed syntax or invalid parameters.
401 Unauthorized: The request requires user authentication or the provided credentials are invalid.
403 Forbidden: The server understood the request, but the user does not have permission to access the requested resource.
404 Not Found: The requested resource could not be found on the server.
500 Internal Server Error: A generic error message indicating that the server encountered an unexpected condition that prevented it from fulfilling the request.
503 Service Unavailable: The server is temporarily unable to handle the request due to maintenance or overload.
To create an API test suite, identify key functionalities, endpoints, and scenarios. Develop test cases using tools like Postman, define request and expected response patterns, handle data-driven testing, organize tests into collections, and structure them logically.
API test design principles:
Equivalence Partitioning: Divide input data into relevant groups that are likely to exhibit similar behaviour.
Boundary Value Analysis: Test at the edges of input ranges to uncover potential issues.
State Transition Testing: Focus on transitions between different states of the API.
Error Guessing: Design tests based on intuition and experience regarding where errors might occur.
Negative Testing: Test with invalid or unexpected inputs to assess error handling.
Dependency Testing: Test scenarios where APIs depend on one another.
Data-Driven Testing: Use various sets of input data to validate different scenarios.
Concurrency Testing: Simulate simultaneous API requests to assess handling of multiple users.
Security Testing: Focus on identifying vulnerabilities and ensuring secure data handling.
Performance Testing: Assess API responsiveness, latency, and scalability.
- When performing API testing, essential checks include validating input and output data, testing various HTTP methods,
- verifying response codes and headers,
- assessing error handling,
- checking authentication and authorization mechanisms,
- evaluating performance under load, and ensuring compliance with API documentation and standards.
Some of the most commonly used tools for API testing are:
- Swagger (OpenAPI)
- Karate DSL
API testing can find a variety of bugs, include:
- Duplicate or missing functionality
- Data Format Errors
- Error handling mechanism is incompatible
- Authentication and Authorization Issues
- Improper messaging
- Multi-threaded issues
- Performance Bottlenecks
- Security issues
Postman is a tool widely used for API testing, allowing users to design, test, and document APIs effectively.
Explanation of 3-Tier Architecture:
The 3-tier architecture is a software design pattern comprising three layers:
Presentation Layer: This layer handles user interface interactions
Logic/Processing Layer: This layer manages business logic
Data Layer: This layer manages business logic
Adding validation points in Postman involves utilizing assertions to verify the correctness of API responses. Follow these steps:
Select a Request: Choose the API request in your Postman collection that you want to validate.
Navigate to “Tests” Section: In the request editor, go to the “Tests” tab.
Test cases commonly used in API testing include:
Positive Testing: Verify correct API behavior with valid inputs.
Negative Testing: Test error handling with invalid inputs or unexpected scenarios.
Boundary Testing: Assess behavior at input limits and edge conditions.
Authentication & Authorization: Validate proper access controls.
Data Validation: Check data formats, types, and integrity.
Response Verification: Ensure accurate response codes, headers, and content.
Performance Testing: Evaluate response times and throughput under load.
Security Testing: Test against common vulnerabilities (e.g., SQL injection).
Concurrency Testing: Simulate simultaneous requests for potential issues.
Compatibility Testing: Ensure compatibility with different devices or browsers.
Version Compatibility: Test backward and forward compatibility.
Error Handling Testing: Verify error messages and codes.
Rate Limiting: Test API behavior when rate limits are exceeded.
Caching: Assess caching mechanisms and cache refresh.
State Management: Validate API’s impact on server/database state.
API testing is favoured over UI testing for automation due to its efficiency, speed, and reduced fragility. APIs offer a stable and controlled interface for testing, enabling faster execution, easier maintenance, and enhanced test coverage. Unlike UI tests, API tests are less affected by visual changes and layout modifications, making them more robust and suitable for continuous integration and regression testing. Additionally, API tests provide quicker feedback on backend functionality and are conducive to parallel execution, optimizing overall testing cycles.
You're testing an API that requires user authentication using JWT tokens. How would you verify that only authorized users can access certain endpoints?
Answer: To verify authorized access, I’d first obtain valid JWT tokens for authorized users. Then, in Postman, I’d set the token in the authorization header of requests to protected endpoints. I’d expect a successful response when authorized and an unauthorized response when using an invalid or missing token.
You are testing an authentication API. Explain how you would use Postman to test different authentication methods like Basic Auth, OAuth2, and API tokens. What considerations should you keep in mind for each method?
Answer: In Postman, for Basic Auth, I’d set the username and password in the request’s authorization settings. For OAuth2, I’d follow the OAuth2 flow using Postman’s OAuth2.0 authorization. For API tokens, I’d include the token in the request headers. I’d ensure correct token management, token expiration, and secure storage.
The API documentation states that the service has rate limiting in place. How would you test the API to ensure that the rate limiting mechanism is working as intended?
Answer: To test rate limiting, I’d create a collection in Postman with requests that exceed the rate limit. I’d then run these requests in quick succession, observing the expected rate-limiting responses (e.g., 429 Too Many Requests) and headers indicating remaining limits and reset times.
Use a tool like JMeter to simulate a large number of concurrent users. This can help you to test the rate limiting under different load conditions.
Use a tool like Charles Proxy to inspect the HTTP requests and responses. This can help you to see the rate limiting headers and to understand how the rate limiting mechanism is working.
The API you are testing requires testing different input data formats, such as JSON and XML. How can you configure Postman to send requests with different content types and validate the responses accordingly?
Answer: In Postman, I’d use the “Headers” section to set the “Content-Type” header for different formats (e.g., “application/json” for JSON, “application/xml” for XML). I’d send requests with corresponding payloads and validate the responses against expected formats.
Suppose an API request results in an error response. How would you verify that the error response contains the correct HTTP status code, a clear error message, and possibly additional relevant information?
Answer: When an error occurs, I’d check the response’s HTTP status code, error message, and possibly the response body for relevant information. I’d compare these against documented specifications to ensure accuracy.
You're testing an API that accepts user input. What strategies would you use to ensure that the API handles both valid and invalid input properly, and that potential security vulnerabilities like SQL injection and XSS attacks are prevented?
Answer: I’d design test cases for various input scenarios, including valid, invalid, and edge cases. For security vulnerabilities, I’d attempt input like SQL injection or XSS attacks. Proper input validation and escaping should prevent these issues.
You need to test APIs with multiple endpoints that have dependencies. How can you use Postman to set up test scenarios that involve chaining requests together and passing data between them?
Answer: Using Postman’s “Tests” scripts and environment variables, I’d extract data from one response and use it in subsequent requests. This allows me to simulate dependencies and data flow between multiple endpoints.
You're testing an API that involves data manipulation operations (e.g., creating, updating, deleting records). How would you ensure data consistency across multiple related API calls?
Answer: To ensure data consistency, I’d create tests that cover different data manipulation scenarios (create, update, delete). I’d verify that related resources are updated consistently and maintain their intended state.
The API supports webhooks to notify external systems of certain events. How would you test the webhook functionality, ensuring that the correct payloads are sent and received?
Answer: I’d set up a testing environment to receive webhooks. Then, I’d trigger the corresponding events in the API and verify that the correct payloads are sent to my test endpoint.
You need to test the API's performance under heavy load. What tools and strategies would you employ to simulate high traffic and measure response times, resource utilization, and potential bottlenecks?
Answer: I’d use tools like Apache JMeter or k6 to simulate heavy traffic by sending concurrent requests. I’d measure response times, CPU/memory usage, and identify potential bottlenecks that could impact performance.
How would you design a testing approach to validate that the API's monitoring and logging mechanisms are capturing relevant information, such as errors, performance metrics, and usage patterns?
Answer: I’d design tests to trigger different scenarios that generate logs or monitoring data. I’d review log outputs and monitoring dashboards to ensure that the expected information is captured accurately.
During testing, you encounter an API response that includes dynamically generated values (e.g., timestamps, session IDs). How can you extract these values from one request's response and use them in subsequent requests within the same Postman collection?
Answer: I’d use Postman’s “Tests” scripts to extract dynamic values from responses and set them as variables. These variables can then be used in subsequent requests to ensure continuity in the testing flow.
Answer: To validate test coverage, I employ techniques like code analysis, traceability matrices, and automation tools. I consider coverage complete when all critical paths, boundary conditions, and use cases are tested and documented.
The API you are testing has rate limiting in place. Describe how you would use Postman to simulate requests that exceed the rate limit and observe how the API behaves. What kind of response codes or messages might you expect?
Answer: In Postman, I’d create a collection with requests that exceed the rate limit. Running these requests should lead to responses indicating rate limiting, such as a 429 status code and appropriate headers.
The API is frequently updated, and you want to automate regression testing. How can you create an automated test suite using Postman collections, Newman (Postman's command-line tool), and continuous integration tools?
Answer: I’d organize test cases into Postman collections and write tests using Postman’s scripting features. Then, I’d use Newman to execute these collections from the command line. Integration with continuous integration tools like Jenkins ensures automated regression testing during each build.