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What does the `wait_time` attribute on a Locust User class control?

Questions in this quiz
  1. What does the `wait_time` attribute on a Locust User class control?
  2. What does the `@task` decorator's optional weight argument (e.g. `@task(3)`) control in a Locust User class?
  3. Why is monitoring response time percentiles (like the 95th or 99th percentile), not just the average, important when interpreting Locust test results?
  4. What is a "Locust User" (an instance of a User class) meant to represent during a test run?
  5. What is Locust?
  6. How can a Locust test simulate different types of users with different behaviors (e.g. a browsing user vs. a purchasing user)?
  7. What metrics does Locust typically report during and after a test run?
  8. What is a reasonable use of Locust's "spawn rate" setting when starting a test?
  9. How does a Locust test scenario typically define simulated user behavior?
  10. Why does Locust's approach of writing test scenarios as regular Python code (rather than a proprietary DSL) offer a practical advantage?
  11. Why might a team run a Locust test in "headless" mode (via command-line flags) rather than through the web UI, particularly in a CI pipeline?
  12. Why might a team choose Locust over a tool with a proprietary GUI-based scripting language (like some traditional commercial load-testing tools)?
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