Performance test planning in Testing Fundamentals - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Key Metric Identification in Performance Testing

Which of the following is the most important metric to monitor during a load test to understand system responsiveness?

A CPU usage percentage

B Response time of requests

C Number of test scripts executed

D Disk space available

💡 Hint

Think about what tells you how fast the system reacts to user actions.

Response time directly measures how quickly the system responds to requests, which is critical for user experience during load.

🧠 Conceptual

intermediate

Purpose of Baseline Testing

What is the main purpose of establishing a baseline in performance test planning?

A To identify the maximum number of users the system can handle

B To verify the correctness of functional requirements

C To test the system's security vulnerabilities

D To measure current system performance under normal conditions

💡 Hint

Baseline is about knowing how the system behaves before changes.

Baseline testing measures system performance under normal conditions to compare against future tests.

❓ framework

advanced

Selecting Performance Test Types

You are planning a performance test for a web application expected to have sudden spikes in user traffic. Which combination of test types should you include to best simulate this scenario?

A Load testing and stress testing

B Baseline testing and usability testing

C Security testing and regression testing

D Functional testing and smoke testing

💡 Hint

Think about tests that simulate normal and extreme user loads.

Load testing simulates expected user traffic, while stress testing pushes beyond limits to see how the system handles spikes.

❓ assertion

advanced

Validating Performance Test Success Criteria

Given a performance test plan with the success criterion: "Response time must be under 2 seconds for 95% of requests." Which assertion correctly validates this criterion after test execution?

Testing Fundamentals

assert (sum(t <= 2 for t in response_times) / len(response_times)) >= 0.95

A assert (sum(t <= 2 for t in response_times) / len(response_times)) >= 0.95

B assert (len([t for t in response_times if t <= 2]) / len(response_times)) >= 0.95

C assert (max(response_times) <= 2)

D assert (sum(response_times) / len(response_times)) < 2

💡 Hint

Focus on counting requests with response time less than or equal to 2 seconds.

Option A correctly counts requests with response time ≤ 2 seconds and checks if they are at least 95% of total.

🔧 Debug

expert

Identifying a Bottleneck from Performance Test Results

After running a performance test, the CPU usage is low, memory usage is stable, but response times increase drastically under load. What is the most likely cause?

A CPU overheating causing throttling

B Memory leaks causing slowdowns

C Network bandwidth saturation causing delays

D Disk space running out causing errors

💡 Hint

Think about what can cause slow responses without high CPU or memory use.

Network bandwidth saturation can cause delays in response times even if CPU and memory are fine.