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TestValid offers a complete Certified Tester AI Testing Exam (CT-AI) practice questions in PDF format. This ISTQB CT-AI test questions pdf file format is simple to use and can be accessed from any device, including a desktop, tablet, laptop, Mac, or smartphone. No matter where you are, you can learn on the go. The PDF version of the Certified Tester AI Testing Exam (CT-AI) exam questions is also readily printable, allowing you to keep tangible copies of the Certified Tester AI Testing Exam (CT-AI) questions with you at all times.
| Certification Vendor: | ISTQB |
|---|---|
| Exam Name: | ISTQB Certified Tester - AI Testing Exam |
| Exam Number: | CT-AI |
| Exam Format: | Multiple-choice questions, 1-2 points per question |
| Certificate Validity Period: | Valid indefinitely (no expiration) |
| Exam Price: | โฌ180 - โฌ250 (varies by region and provider) |
| Related Certifications: | ISTQB Certified Tester Foundation Level (CTFL) ISTQB Certified Tester Testing with Generative AI (CT-GenAI) |
| Exam Duration: | 60 (75 for non-native language) |
| Real Exam Qty: | 40 |
| Passing Score: | 65% (29/44 points for v2.0; 31/47 points for v1.0) |
| Available Languages: | German, Portuguese, Japanese, French, Chinese, English, Korean, Spanish |
| Recommended Training: | CT-AI Syllabus v2.0 ISTQB Accredited Training Providers |
| Exam Registration: | Pearson VUE iSQI Exam Registration ISTQB Official Registration |
| Sample Questions: | ISTQB CT-AI Sample Questions |
| Exam Way: | Online remote proctored / Onsite test center |
| Pre Condition: | Must hold ISTQB Certified Tester Foundation Level (CTFL) certification |
| Official Syllabus URL: | https://istqb.org/certifications/certified-tester-ai-testing-ct-ai/ |
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NEW QUESTION # 28
"Splendid Healthcare" has started developing a cancer detection system based on ML. The type of cancer they plan on detecting has 2% prevalence rate in the population of a particular geography. It is required that the model performs well for both normal and cancer patients. Which ONE of the following combinations requires MAXIMIZATION?
Answer: C
Explanation:
Prevalence Rate and Model Performance:
The cancer detection system being developed by "Splendid Healthcare" needs to account for the fact that the type of cancer has a 2% prevalence rate in the population. This indicates that the dataset is highly imbalanced with far fewer positive (cancer) cases compared to negative (normal) cases.
Importance of Recall:
Recall, also known as sensitivity or true positive rate, measures the proportion of actual positive cases that are correctly identified by the model. In medical diagnosis, especially cancer detection, recall is critical because missing a positive case (false negative) could have severe consequences for the patient. Therefore, maximizing recall ensures that most, if not all, cancer cases are detected.
Importance of Precision:
Precision measures the proportion of predicted positive cases that are actually positive. High precision reduces the number of false positives, meaning fewer people will be incorrectly diagnosed with cancer. This is also important to avoid unnecessary anxiety and further invasive testing for those who do not have the disease.
Balancing Recall and Precision:
In scenarios where both false negatives and false positives have significant consequences, it is crucial to balance recall and precision. This balance ensures that the model is not only good at detecting positive cases but also accurate in its predictions, reducing both types of errors.
Accuracy and Specificity:
While accuracy (the proportion of total correct predictions) is important, it can be misleading in imbalanced datasets. In this case, high accuracy could simply result from the model predicting the majority class (normal) correctly. Specificity (true negative rate) is also important, but for a cancer detection system, recall and precision take precedence to ensure positive cases are correctly and accurately identified.
Conclusion:
Therefore, for a cancer detection system with a low prevalence rate, maximizing both recall and precision is crucial to ensure effective and accurate detection of cancer cases.
NEW QUESTION # 29
A beer company is trying to understand how much recognition its logo has in the market. It plans to do that by monitoring images on various social media platforms using a pre-trained neural network for logo detection.
This particular model has been trained by looking for words, as well as matching colors on social media images. The company logo has a big word across the middle with a bold blue and magenta border.
Which associated risk is most likely to occur when using this pre-trained model?
Answer: D
Explanation:
According to the syllabus, pre-trained models often inherit biases and limitations from the data and processes used in their original training, which may not align with the new use case. Specifically, the syllabus states:
"When using a pre-trained model, the training data and process cannot be fully controlled or known by the user of the model. As a result, the model can inherit biases or inaccuracies that were part of its original development and training process." (Reference: ISTQB CT-AI Syllabus v1.0, Section 1.8.3)
NEW QUESTION # 30
You have been developing test automation for an e-commerce system. One of the problems you are seeing is that object recognition in the GUI is having frequent failures. You have determined this is because the developers are changing the identifiers when they make code updates. How could AI help make the automation more reliable?
Answer: C
Explanation:
The syllabus discusses using AI-based tools to reduce GUI test brittleness:
"AI can be used to reduce the brittleness of this approach, by employing AI-based tools to identify the correct objects using various criteria (e.g., XPath, label, id, class, X/Y coordinates), and to choose the historically most stable identification criteria." (Reference: ISTQB CT-AI Syllabus v1.0, Section 11.6.1)
NEW QUESTION # 31
Which of the following aspects is a challenge when handling test data for an AI-based system?
Answer: B
Explanation:
Handlingtest datain AI-based systems presents numerous challenges, particularly in terms ofdata privacy and confidentiality. AI models often require vast amounts of training data, some of which may containpersonal, sensitive, or confidential information. Ensuringcompliance with data protection laws (e.g., GDPR, CCPA)and implementingsecure data-handling practicesis a major challenge in AI testing.
* Data Privacy Regulations
* AI-based systems frequently process personal data, such as images, names, and transaction details, leading toprivacy concerns.
* Compliance with regulations such asGDPR (General Data Protection Regulation)andCCPA (California Consumer Privacy Act)requiresproper anonymization, encryption, or redactionof sensitive data before using it for testing.
* Data Security Challenges
* AI models mayleak confidential informationif proper security measures are not in place.
* Protectingtraining and test data from unauthorized accessis crucial to maintainingtrust and compliance.
* Legal and Ethical Considerations
* Organizations mustobtain legal approvalbefore using certain datasets, especially those containinghealth records, financial data, or personally identifiable information (PII).
* Testers may need toemploy synthetic dataordata maskingtechniques to minimize exposure risks.
* (B) Output data or intermediate data#
* While analyzing output data is important, it does notpose a significant challengecompared to handlingpersonal or confidential test data.
* (C) Video frame speed or aspect ratio#
* These aretechnical challengesin processing AI models but do not fall underdata privacy or ethical considerations.
* (D) Data frameworks or machine learning frameworks#
* Choosing an appropriateML framework (e.g., TensorFlow, PyTorch)is important, but it is nota major challenge related to test data handling.
* Handling personal or confidential data is a critical challenge in AI testing"Personal or otherwise confidential data may need special techniques for sanitization, encryption, or redaction.Legal approval for use may also be required." Why is Option A Correct?Why Other Options are Incorrect?References from ISTQB Certified Tester AI Testing Study GuideThus,option A is the correct answer, asdata privacy and confidentiality are major challenges when handling test data for AI-based systems.
NEW QUESTION # 32
Which of the following is a problem with AI-generated test cases that are generated from the requirements?
Answer: C
Explanation:
The syllabus mentions a drawback of AI-generated test cases:
"AI-based test generation tools can generate test cases... However, unless a test model that defines required behaviors is used as the basis of the tests, this form of test generation generally suffers from a test oracle problem because the AI-based tool does not know what the expected results should be." (Reference: ISTQB CT-AI Syllabus v1.0, Section 11.3, page 78 of 99)
NEW QUESTION # 33
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