AI in Testing interview questions — Easy
224 easy-level AI in Testing questions from our Automation Testing bank, each with the correct answer and an explanation of why it is correct.
What this covers
AI in Testing appears throughout Automation Testing interviews. At easy level, interviewers are typically checking that you know the fundamentals and can describe them precisely — definitions, defaults, and the basic mechanics. Work through these, then explain your answer out loud; the second part is what interviews actually test.
20 example questions
1. In AI in Testing, what does hallucination risk do or refer to?
- A. AI system that plans and executes testing toward a goal.
- B. Workflow where people review and approve AI output.
- C. Selecting tests affected by a specific code change.
- D. Tendency of generative AI to produce plausible but incorrect output.correct
Why: hallucination risk: Tendency of generative AI to produce plausible but incorrect output.
2. In AI in Testing, what does human-in-the-loop do or refer to?
- A. Grouping similar test failures to speed up triage.
- B. Selecting tests affected by a specific code change.
- C. Workflow where people review and approve AI output.correct
- D. Tendency of generative AI to produce plausible but incorrect output.
Why: human-in-the-loop: Workflow where people review and approve AI output.
3. Which of the following best describes test impact analysis in AI in Testing?
- A. Selecting tests affected by a specific code change.correct
- B. Open protocol connecting AI models to tools such as a Playwright browser.
- C. Mechanism that repairs broken element selectors using alternative attributes.
- D. ML-based comparison of UI screenshots tolerant to noise.
Why: test impact analysis: Selecting tests affected by a specific code change.
4. In the context of AI in Testing, self-healing locator is best described as:
- A. Mechanism that repairs broken element selectors using alternative attributes.correct
- B. Large language model used to generate or review test artifacts.
- C. Workflow where people review and approve AI output.
- D. Artificially generated data that mimics production characteristics.
Why: self-healing locator: Mechanism that repairs broken element selectors using alternative attributes.
5. In AI in Testing, what does self-healing locator do or refer to?
- A. Mechanism that repairs broken element selectors using alternative attributes.correct
- B. Workflow where people review and approve AI output.
- C. Selecting tests affected by a specific code change.
- D. Tendency of generative AI to produce plausible but incorrect output.
Why: self-healing locator: Mechanism that repairs broken element selectors using alternative attributes.
6. In AI in Testing, what does MCP (Model Context Protocol) do or refer to?
- A. Grouping similar test failures to speed up triage.
- B. Open protocol connecting AI models to tools such as a Playwright browser.correct
- C. Selecting tests affected by a specific code change.
- D. Large language model used to generate or review test artifacts.
Why: MCP (Model Context Protocol): Open protocol connecting AI models to tools such as a Playwright browser.
7. In AI in Testing, what does test impact analysis do or refer to?
- A. Tendency of generative AI to produce plausible but incorrect output.
- B. Workflow where people review and approve AI output.
- C. AI system that plans and executes testing toward a goal.
- D. Selecting tests affected by a specific code change.correct
Why: test impact analysis: Selecting tests affected by a specific code change.
8. Which of the following best describes failure clustering in AI in Testing?
- A. ML-based comparison of UI screenshots tolerant to noise.
- B. Grouping similar test failures to speed up triage.correct
- C. Artificially generated data that mimics production characteristics.
- D. Open protocol connecting AI models to tools such as a Playwright browser.
Why: failure clustering: Grouping similar test failures to speed up triage.
9. Which of the following best describes visual AI in AI in Testing?
- A. Mechanism that repairs broken element selectors using alternative attributes.
- B. Large language model used to generate or review test artifacts.
- C. Selecting tests affected by a specific code change.
- D. ML-based comparison of UI screenshots tolerant to noise.correct
Why: visual AI: ML-based comparison of UI screenshots tolerant to noise.
10. In AI in Testing, what does failure clustering do or refer to?
- A. ML-based comparison of UI screenshots tolerant to noise.
- B. Large language model used to generate or review test artifacts.
- C. AI system that plans and executes testing toward a goal.
- D. Grouping similar test failures to speed up triage.correct
Why: failure clustering: Grouping similar test failures to speed up triage.
11. Which of the following best describes synthetic data in AI in Testing?
- A. Tendency of generative AI to produce plausible but incorrect output.
- B. Artificially generated data that mimics production characteristics.correct
- C. Selecting tests affected by a specific code change.
- D. Workflow where people review and approve AI output.
Why: synthetic data: Artificially generated data that mimics production characteristics.
12. Which of the following best describes self-healing locator in AI in Testing?
- A. Mechanism that repairs broken element selectors using alternative attributes.correct
- B. Open protocol connecting AI models to tools such as a Playwright browser.
- C. Large language model used to generate or review test artifacts.
- D. Grouping similar test failures to speed up triage.
Why: self-healing locator: Mechanism that repairs broken element selectors using alternative attributes.
13. What is the purpose of test impact analysis in AI in Testing?
- A. Mechanism that repairs broken element selectors using alternative attributes.
- B. Selecting tests affected by a specific code change.correct
- C. Tendency of generative AI to produce plausible but incorrect output.
- D. AI system that plans and executes testing toward a goal.
Why: test impact analysis: Selecting tests affected by a specific code change.
14. In AI in Testing, what does autonomous test agent do or refer to?
- A. AI system that plans and executes testing toward a goal.correct
- B. Selecting tests affected by a specific code change.
- C. Open protocol connecting AI models to tools such as a Playwright browser.
- D. Mechanism that repairs broken element selectors using alternative attributes.
Why: autonomous test agent: AI system that plans and executes testing toward a goal.
15. Which of the following best describes human-in-the-loop in AI in Testing?
- A. Workflow where people review and approve AI output.correct
- B. Large language model used to generate or review test artifacts.
- C. Mechanism that repairs broken element selectors using alternative attributes.
- D. ML-based comparison of UI screenshots tolerant to noise.
Why: human-in-the-loop: Workflow where people review and approve AI output.
16. Which of the following best describes MCP (Model Context Protocol) in AI in Testing?
- A. Workflow where people review and approve AI output.
- B. Selecting tests affected by a specific code change.
- C. Open protocol connecting AI models to tools such as a Playwright browser.correct
- D. Large language model used to generate or review test artifacts.
Why: MCP (Model Context Protocol): Open protocol connecting AI models to tools such as a Playwright browser.
17. What is the purpose of self-healing locator in AI in Testing?
- A. Tendency of generative AI to produce plausible but incorrect output.
- B. Grouping similar test failures to speed up triage.
- C. Mechanism that repairs broken element selectors using alternative attributes.correct
- D. Artificially generated data that mimics production characteristics.
Why: self-healing locator: Mechanism that repairs broken element selectors using alternative attributes.
18. Which of the following best describes autonomous test agent in AI in Testing?
- A. Workflow where people review and approve AI output.
- B. AI system that plans and executes testing toward a goal.correct
- C. Tendency of generative AI to produce plausible but incorrect output.
- D. Grouping similar test failures to speed up triage.
Why: autonomous test agent: AI system that plans and executes testing toward a goal.
19. What is the purpose of autonomous test agent in AI in Testing?
- A. AI system that plans and executes testing toward a goal.correct
- B. Artificially generated data that mimics production characteristics.
- C. Workflow where people review and approve AI output.
- D. Mechanism that repairs broken element selectors using alternative attributes.
Why: autonomous test agent: AI system that plans and executes testing toward a goal.
20. What is the purpose of MCP (Model Context Protocol) in AI in Testing?
- A. Artificially generated data that mimics production characteristics.
- B. Open protocol connecting AI models to tools such as a Playwright browser.correct
- C. AI system that plans and executes testing toward a goal.
- D. Large language model used to generate or review test artifacts.
Why: MCP (Model Context Protocol): Open protocol connecting AI models to tools such as a Playwright browser.