Introduction – Why API Performance Testing Is Important in Interviews
In modern applications, performance issues rarely come from the UI alone. Most bottlenecks occur at the API and backend level, where thousands or millions of requests are processed every minute.
That’s why interviewers increasingly focus on api performance testing interview questions to evaluate whether a candidate can:
- Identify API bottlenecks under load
- Understand response time, throughput, and scalability
- Design performance test scenarios for REST/SOAP APIs
- Analyze failures like timeouts, throttling, and server crashes
- Work with tools such as JMeter, Postman, Gatling, Rest Assured, Python
This article is a complete interview-focused guide with clear explanations, real-time examples, response samples, and scenario-based questions, suitable for freshers to experienced performance and QA engineers.
What Is API Testing? (Clear & Simple)
API testing verifies whether APIs work correctly by checking:
- Requests and responses
- Status codes
- Response data
- Headers and authentication
API performance testing goes one step further and checks:
- How fast the API responds
- How many requests it can handle
- How it behaves under peak load and stress
Simple Example
For a Search API:
- Functional test → correct search results
- Performance test → response < 2 seconds for 1,000 concurrent users
REST vs SOAP vs GraphQL (Performance Perspective)
| Feature | REST | SOAP | GraphQL |
| Payload Size | Lightweight | Heavy (XML) | Optimized |
| Performance | Fast | Slower | Efficient |
| Caching | Easy | Difficult | Client-driven |
| Usage | Most APIs | Legacy systems | Modern microservices |
👉 Most api performance testing interview questions focus on REST APIs, but SOAP performance knowledge is useful in enterprise projects.
API Performance Testing Interview Questions & Answers (90+)
Section 1: API Performance Basics (Q1–Q20)
1. What is API performance testing?
Testing APIs to measure response time, throughput, scalability, and stability under load.
2. Why is API performance testing important?
Slow APIs impact user experience, revenue, and system stability.
3. Difference between functional and performance testing?
Functional testing checks correctness; performance testing checks speed and scalability.
4. What are common API performance metrics?
Response time, throughput, latency, error rate, CPU/memory usage.
5. What is response time?
Time taken by API to respond to a request.
6. What is throughput?
Number of requests processed per second.
7. What is latency?
Delay between request and first byte of response.
8. What is scalability testing?
Testing how API behaves when load increases.
9. What is load testing?
Testing API under expected user load.
10. What is stress testing?
Testing API beyond capacity to find breaking point.
11. What is spike testing?
Sudden increase in traffic to test resilience.
12. What is endurance testing?
Running API under load for long duration.
13. What is volume testing?
Testing API with large amounts of data.
14. What is SLA in API performance?
Service Level Agreement defining acceptable response times.
15. What is timeout?
Maximum time client waits for API response.
16. What is rate limiting?
Restricting number of requests per client.
17. What is throttling?
Controlling API traffic to protect backend.
18. What is API caching?
Storing responses to improve performance.
19. What is concurrency?
Multiple users accessing API simultaneously.
20. What is bottleneck?
Component that limits overall performance.
HTTP Status Codes – Important for Performance Testing
| Code | Meaning | Performance Context |
| 200 | OK | Successful response |
| 201 | Created | Resource created |
| 204 | No Content | Fast success |
| 400 | Bad Request | Client error |
| 401 | Unauthorized | Auth overhead |
| 403 | Forbidden | Access denied |
| 404 | Not Found | Invalid routing |
| 429 | Too Many Requests | Rate limiting |
| 500 | Server Error | Backend overload |
| 503 | Service Unavailable | System under stress |
Section 2: API Performance Testing Concepts (Q21–Q45)
21. What validations are done in API performance testing?
Response time, throughput, error rate, resource utilization.
22. Is functional correctness important in performance tests?
Yes, incorrect responses invalidate performance results.
23. What is baseline performance?
Normal performance under minimal load.
24. What is peak load?
Maximum expected traffic.
25. What is saturation point?
Load at which performance degrades.
26. What is TPS?
Transactions per second.
27. What is RPS?
Requests per second.
28. What is think time?
Delay between user actions.
29. What is warm-up period?
Initial time before measurements stabilize.
30. What is cool-down period?
Time after test completion.
31. What is API payload size impact?
Larger payloads increase response time.
32. What is connection pooling?
Reusing connections to reduce overhead.
33. What is keep-alive?
Persistent HTTP connection.
34. What is network latency?
Delay caused by network.
35. What is server-side profiling?
Analyzing backend resource usage.
36. What is client-side overhead?
Delay caused by client tool.
37. What is performance regression testing?
Detecting performance degradation after changes.
38. What is API monitoring?
Continuous performance tracking in production.
39. What is synthetic monitoring?
Simulated traffic monitoring.
40. What is real user monitoring (RUM)?
Tracking real user behavior.
41. What is load distribution?
Balancing traffic across servers.
42. What is horizontal scaling?
Adding more servers.
43. What is vertical scaling?
Increasing server resources.
44. What is database performance impact?
Slow queries affect API response.
45. What is garbage collection impact?
GC pauses can slow APIs.
Real-Time API Performance Validation Example
Sample Request
GET /api/products?search=laptop
Sample Response
{
“count”: 120,
“items”: [
{ “id”: 1, “name”: “Laptop A” },
{ “id”: 2, “name”: “Laptop B” }
]
}
Performance Validations
- Average response time < 1.5 seconds
- Error rate < 1%
- Throughput ≥ 500 RPS
- No 5xx errors under load
Tools & Automation Snippets (Interview-Relevant)
Postman (Basic Performance Check)
pm.test(“Response time < 2000ms”, function () {
pm.expect(pm.response.responseTime).to.be.below(2000);
});
JMeter (Conceptual Use)
- Thread Group → Users
- HTTP Request Sampler → API
- Listener → Response Time Graph
Rest Assured + Timing
given()
.when()
.get(“/products”)
.then()
.time(lessThan(2000L));
Python (Simple Load Loop)
import requests, time
start = time.time()
for i in range(100):
requests.get(url)
print(“Total time:”, time.time() – start)
Section 3: Advanced API Performance Interview Questions (Q46–Q70)
46. How do you design an API performance test?
Identify endpoints, load profile, metrics, and success criteria.
47. How do you select test data?
Based on real production data patterns.
48. How do you simulate concurrency?
Using threads or virtual users.
49. How do you identify performance bottlenecks?
Analyze logs, metrics, and response times.
50. How do you handle authentication in performance tests?
Reuse tokens or disable auth where appropriate.
51. What is token refresh overhead?
Extra latency due to token renewal.
52. How do you test rate limiting?
Send high RPS and expect 429 responses.
53. How do you validate caching?
Compare first vs subsequent response times.
54. How do you test database-heavy APIs?
Simulate realistic data volume and concurrency.
55. How do you test file upload APIs?
Large payload and concurrent uploads.
56. How do you test pagination performance?
High page numbers and large datasets.
57. How do you test search API performance?
Complex queries under load.
58. How do you test third-party API dependencies?
Mock or simulate latency.
59. How do you analyze GC issues?
Monitor heap and GC logs.
60. How do you correlate backend metrics?
Map API latency to CPU, memory, DB.
61. How do you tune performance?
Caching, indexing, scaling.
62. How do you report performance results?
Graphs, SLAs, pass/fail criteria.
63. What is chaos testing?
Introducing failures to test resilience.
64. What is circuit breaker?
Stops calls to failing services.
65. What is retry storm?
Excess retries causing overload.
66. What is bulkhead pattern?
Isolating resources.
67. What is performance budget?
Maximum allowed latency.
68. What is API gateway impact?
Routing and security overhead.
69. What is compression impact?
Reduced payload vs CPU cost.
70. What is HTTP/2 impact?
Better multiplexing improves performance.
Scenario-Based API Performance Testing Interview Questions (15)
- API is fast for 10 users but slow for 1,000—why?
- Response time increases gradually during test—possible causes?
- Sudden spike causes 503 errors—how analyze?
- Only POST APIs are slow—why?
- API fast locally but slow in production—reasons?
- Database CPU high during load—what check?
- Caching enabled but no improvement—why?
- Token generation becomes bottleneck—solution?
- Search API slow for large datasets—fix?
- Third-party API delays—how mitigate?
- Memory usage grows continuously—what issue?
- GC pauses observed—impact?
- Rate limiting not working—how test?
- Load test passes but users complain—why?
- Performance degrades after release—next steps?
How Interviewers Evaluate Your Answers
Interviewers look for:
- Clear understanding of performance metrics
- Real project examples
- Logical analysis of bottlenecks
- Knowledge of tools and results interpretation
- Ability to explain trade-offs
👉 Analysis mindset matters more than tool names.
API Performance Testing Interview Cheatsheet
- Measure response time & throughput
- Always validate correctness
- Identify bottlenecks logically
- Use realistic load patterns
- Correlate API & backend metrics
- Focus on scalability and stability
FAQs – API Performance Testing Interview Questions
Q1. Is API performance testing different from UI performance testing?
Yes, API tests are faster, more stable, and isolate backend issues.
Q2. Which tool is best for API performance testing?
JMeter is most common; Gatling and k6 are also popular.
Q3. Do freshers get performance questions?
Yes, basic concepts and metrics are expected.
Q4. Biggest mistake candidates make?
Ignoring backend metrics and focusing only on response time.
Q5. How to prepare quickly?
Practice basic load tests and analyze results.