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| Certification Vendor: | ISTQB |
|---|---|
| Exam Name: | ISTQB Certified Tester - AI Testing |
| Exam Number: | CT-AI |
| Passing Score: | 65% |
| Exam Duration: | 60 minutes |
| Exam Price: | EUR 250 |
| Related Certifications: | ISTQB CTFL ISTQB CTAL-TTA |
| Real Exam Qty: | 40 |
| Available Languages: | English |
| Exam Format: | Multiple Choice |
| Certificate Validity Period: | Lifetime (no expiration) |
| Sample Questions: | ISTQB CT-AI Sample Questions |
| Exam Way: | Online proctored or in-person at authorized testing centers |
| Pre Condition: | ISTQB CTFL (Certified Tester Foundation Level) certification is recommended but not mandatory |
| Official Syllabus URL: | https://www.istqb.org/certifications/artificial-intelligence-testing-certification |
CT-AI practice questions and pass it with confidence. As far as the top features of CT-AI exam dumps are concerned, these ISTQB CT-AI latest questions are real and verified by ISTQB CT-AI certification exam experts. With the ISTQB CT-AI Practice Test questions you will get everything that you need to learn, prepare and get success in the final Certified Tester AI Testing Exam certification exam.
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NEW QUESTION # 38
Which characteristic of AI-based systems makes it difficult to ensure they are safe (e.g., not harming humans)?
Answer: C
Explanation:
The ISTQB CT-AI syllabus lists several characteristics that make it difficult to ensure safety in AI- based systems. Section2.8 - Safety and AIexplicitly names the characteristics that complicate safety assurance:complexity,non-determinism,probabilistic behavior,self-learning,lack of transparency, andlack of robustness. Among these,complexityis a core challenge because modern AI systems--particularly those using deep learning--have highly non-linear behavior, large numbers of parameters, and intricate interactions that are hard to predict.
Option B (Complexity) directly aligns with the syllabus and is therefore correct.
NEW QUESTION # 39
Which ONE of the following would be the MOST effective input to an AI based defect prediction tool?
Answer: C
Explanation:
The correct answer is A. Developers associated with previous code changes . The CT-AI syllabus explains that defect prediction may be based on source code metrics, process metrics, and people or organizational metrics. It further states that defect prediction is most effective when based on prior experience in a similar situation, such as the same code base or the same developers. Most importantly, it notes that the best predictors have been found to be people and organizational measures , rather than commonly used source- code metrics such as lines of code and cyclomatic complexity.
Options B, C, and D may still be useful. Cyclomatic complexity and lines of code changed are common code or change metrics, while commit size is a process/change metric. However, the question asks for the most effective input, and the syllabus explicitly prioritizes people and organizational measures. Developer association with prior changes can reveal patterns involving ownership, familiarity, change history, and previous defect introduction, making it the strongest input among the available options.
References/topics: CT-AI Syllabus Chapter 11, Section 11.5 "Using AI for Defect Prediction."
NEW QUESTION # 40
A local business has a mail pickup/delivery robot for their office. The robot currently uses a track to move between pickup/drop off locations. When it arrives at a destination, the robot stops to allow a human to remove or deposit mail.
The office has decided to upgrade the robot to include AI capabilities that allow the robot to perform its duties without a track, without running into obstacles, and without human intervention.
The test team is creating a list of new and previously established test objectives and acceptance criteria to be used in the testing of the robot upgrade. Which of the following test objectives will test an AI quality characteristic for this system?
Answer: B
Explanation:
In the syllabus, theevolutioncharacteristic for AI-based systems means the ability of the system to evolve and adapt its behavior in response to changes in the environment or in its own performance:
"Evolution is the system's ability to change itself to adapt to new situations, different hardware, or a changing operational environment."
NEW QUESTION # 41
A test engineer is planning testing for a wearable medical device using AI. The medical device will detect possible heart issues in patients and dispatch emergency services automatically. It is not expected that many patients will have heart issues, and this is reflected in the available data. In this case, it is decided it is more important that emergency services are not sent un-necessarily, than it is to detect actual heart problems.
Which ONE of the following metrics should the test engineer choose to ensure that the emergency services are sent only when needed?
Answer: D
Explanation:
The correct answer is D. Precision . In this scenario, a positive prediction means that the system detects a possible heart issue and dispatches emergency services. The stated priority is to avoid sending emergency services unnecessarily. That means the test engineer must minimize false positives , because a false positive would incorrectly classify a patient as needing emergency assistance. The CT-AI syllabus states that precision is suitable when the cost of false positives is high and confidence in positive outcomes needs to be high.
Accuracy is a weak choice because the data is imbalanced: most patients are not expected to have heart issues, so a model could appear accurate while still making unacceptable emergency-dispatch errors. MSE and R- squared are regression metrics, not classification metrics, and the problem is a classification decision: dispatch or do not dispatch. Recall would be appropriate if the main concern were not missing true heart issues, but the question explicitly prioritizes avoiding unnecessary dispatches. Therefore, precision is the best functional performance metric for this acceptance objective.
References/topics: CT-AI Syllabus Chapter 5, Sections 5.1 and 5.4 "Selecting ML Functional Performance Metrics."
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NEW QUESTION # 42
"AllerEgo" is a product that uses sell-learning to predict the behavior of a pilot under combat situation for a variety of terrains and enemy aircraft formations. Post training the model was exposed to the real- world data and the model was found to be behaving poorly. A lot of data quality tests had been performed on the data to bring it into a shape fit for training and testing.
Which ONE of the following options is least likely to describes the possible reason for the fall in the performance, especially when considering the self-learning nature of the Al system?
SELECT ONE OPTION
* The difficulty of defining criteria for improvement before the model can be accepted.
* The fast pace of change did not allow sufficient time for testing.
* The unknown nature and insufficient specification of the operating environment might have caused the poor performance.
* There was an algorithmic bias in the Al system.
Answer:
Explanation:
* A. The difficulty of defining criteria for improvement before the model can be accepted.
* Defining criteria for improvement is a challenge in the acceptance of AI models, but it is not directly related to the performance drop in real-world scenarios. It relates more to the evaluation and deployment phase rather than affecting the model's real-time performance post-deployment.
* B. The fast pace of change did not allow sufficient time for testing.
* This can significantly affect the model's performance. If the system is self-learning, it needs to adapt quickly, and insufficient testing time can lead to incomplete learning and poor performance.
* C. The unknown nature and insufficient specification of the operating environment might have caused the poor performance.
* This is highly likely to affect performance. Self-learning AI systems require detailed specifications of the operating environment to adapt and learn effectively. If the environment is insufficiently specified, the model may fail to perform accurately in real-world scenarios.
* D. There was an algorithmic bias in the AI system.
* Algorithmic bias can significantly impact the performance of AI systems. If the model has biases, it will not perform well across different scenarios and data distributions.
Given the context of the self-learning nature and the need for real-time adaptability, optionAis least likely to describe the fall in performance because it deals with acceptance criteria rather than real-time performance issues.
NEW QUESTION # 43
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