A company is developing an AI-based system for autonomous tractors. They want to validate the system's behavior in various field conditions, including different soil types and weather. Which approach is most effective for this validation?

Manual inspection of the system

Unit testing of individual algorithms

A company is developing an AI-based system for autonomous ships. They want to verify that the system can safely navigate through various weather conditions and traffic scenarios. Which approach is most suitable for validating the system's behavior in these varied conditions?

Unit testing of individual components

A team is testing an autonomous system for self-driving cars. They have access to a large dataset of real-world driving scenarios, but the dataset is biased towards common situations. The team is considering using simulation to generate rare scenarios. Which approach is most effective for validating the system's behavior in rare scenarios?

Use only simulation, as it can cover all scenarios.

Use the real-world dataset as-is, as it is the most accurate representation.

Use simulation to generate rare scenarios and combine with real-world data.

Use manual testing to cover rare scenarios.

A team is testing an AI-based system for autonomous drones that must navigate through forests. They are finding it difficult to predict all possible scenarios the drone might encounter. Which testing challenge is most prominent in this situation?

Difficulty in measuring performance

Unpredictability of environmental interactions

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Domain 2Objective 2

Autonomy CT-AI Practice Questions (Page 3)

Part of the Domain 2: Quality Characteristics for AI-Based Systems domain, which makes up ~12% of our current practice bank. ISTQB does not publish an official question count, but from its 60-minute exam (~25–40 total, ~3–5 in this domain), expect 1–1 from this objective — we provide 24 practice questions to prepare you well beyond it. (estimate)

24questions here

5free pages

6concepts

Questions 11–15

11application · medium
A company is developing an AI-based system for autonomous tractors. They want to validate the system's behavior in various field conditions, including different soil types and weather. Which approach is most effective for this validation?
Select an answer first
12application · medium
A company is developing an AI-based system for autonomous ships. They want to verify that the system can safely navigate through various weather conditions and traffic scenarios. Which approach is most suitable for validating the system's behavior in these varied conditions?
Select an answer first
13expert · hard
A team is testing an autonomous system for self-driving cars. They have access to a large dataset of real-world driving scenarios, but the dataset is biased towards common situations. The team is considering using simulation to generate rare scenarios. Which approach is most effective for validating the system's behavior in rare scenarios?
Select an answer first
14application · medium
A team is testing an AI-based system for autonomous drones that must navigate through forests. They are finding it difficult to predict all possible scenarios the drone might encounter. Which testing challenge is most prominent in this situation?
Select an answer first
15application · medium
An autonomous system for a power grid is being developed. The system must make real-time decisions to prevent blackouts. The team is concerned about the system's ability to respond quickly and accurately. Which quality characteristic is most critical here?
Select an answer first

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Autonomy CT-AI Practice Questions (Page 3)

Questions 11–15

A company is developing an AI-based system for autonomous tractors. They want to validate the system's behavior in various field conditions, including different soil types and weather. Which approach is most effective for this validation?

A company is developing an AI-based system for autonomous ships. They want to verify that the system can safely navigate through various weather conditions and traffic scenarios. Which approach is most suitable for validating the system's behavior in these varied conditions?

A team is testing an AI-based system for autonomous drones that must navigate through forests. They are finding it difficult to predict all possible scenarios the drone might encounter. Which testing challenge is most prominent in this situation?

An autonomous system for a power grid is being developed. The system must make real-time decisions to prevent blackouts. The team is concerned about the system's ability to respond quickly and accurately. Which quality characteristic is most critical here?

Keep studying — Domain 2: Quality Characteristics for AI-Based Systems